3,025 results on '"brain–computer interface (BCI)"'
Search Results
2. Joint hybrid recursive feature elimination based channel selection and ResGCN for cross session MI recognition.
- Author
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Li, Duan, Li, Keyun, Xia, Yongquan, Dong, Jianhua, and Lu, Ronglei
- Subjects
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GRAPH neural networks , *CONVOLUTIONAL neural networks , *MOTOR imagery (Cognition) , *RECOGNITION (Psychology) , *BRAIN-computer interfaces - Abstract
In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
3. Transforming Motor Imagery Analysis: A Novel EEG Classification Framework Using AtSiftNet Method.
- Author
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Xu, Haiqin, Haider, Waseem, Aziz, Muhammad Zulkifal, Sun, Youchao, and Yu, Xiaojun
- Abstract
This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350 × 18 ). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946 % ), Mutual Information (i.e., 98.902 % ), Independent Component Analysis (i.e., 99.62 % ), and Principal Component Analysis (i.e., 98.884 % ) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89 % . The experiments' findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain–Computer Interfaces (BCI). [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. EEG-based Incongruency Decoding in AR with sLDA, SVM, and EEGNet.
- Author
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Wimmer, Michael, Veas, Eduardo E., and Müller-Putz, Gernot R.
- Subjects
ELECTROENCEPHALOGRAPHY ,AUGMENTED reality ,BRAIN-computer interfaces ,CONVOLUTIONAL neural networks ,DEEP learning - Abstract
Augmented reality (AR) technologies enhance a user's physical environment by providing contextual information about their surroundings. This information might appear incongruent to users, either due to their current mental context or factual errors in the data. This paper explores the feasibility of incongruency decoding using electroencephalographic (EEG) signals from 19 participants acquired during an interactive AR task. Previous studies on single-trial N400 decoding for brain-computer interfaces using EEG data are limited. Therefore, we implemented commonly used classification approaches and assessed their decoding performance compared to the convolutional neural network EEGNet. We found that the investigated approaches offer comparable accuracies ranging from 63.3% to 64.8%. Successful decoding of incongruency effects can foster more contextually appropriate interactions within AR environments. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. EEG-VTTCNet: A loss joint training model based on the vision transformer and the temporal convolution network for EEG-based motor imagery classification.
- Author
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Shi, Xingbin, Li, Baojiang, Wang, Wenlong, Qin, Yuxin, Wang, Haiyan, and Wang, Xichao
- Subjects
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TRANSFORMER models , *MOTOR imagery (Cognition) , *BRAIN-computer interfaces , *TIME-varying networks , *FEATURE extraction - Abstract
• The increase of decoding precision of motor imaging EEG signal. • The hybrid TCN and ViT method achieves good results. • A shared convolution strategy and a dual-branching strategy. Brain-computer interface (BCI) is a technology that directly connects signals between the human brain and a computer or other external device. Motor imagery electroencephalographic (MI-EEG) signals are considered a promising paradigm for BCI systems, with a wide range of potential applications in medical rehabilitation, human–computer interaction, and virtual reality. Accurate decoding of MI-EEG signals poses a significant challenge due to issues related to the quality of the collected EEG data and subject variability. Therefore, developing an efficient MI-EEG decoding network is crucial and warrants research. This paper proposes a loss joint training model based on the vision transformer (VIT) and the temporal convolutional network (EEG-VTTCNet) to classify MI-EEG signals. To take advantage of multiple modules together, the EEG-VTTCNet adopts a shared convolution strategy and a dual-branching strategy. The dual-branching modules perform complementary learning and jointly train shared convolutional modules with better performance. We conducted experiments on the BCI Competition IV-2a and IV-2b datasets, and the proposed network outperformed the current state-of-the-art techniques with an accuracy of 84.58% and 90.94%, respectively, for the subject-dependent mode. In addition, we used t-SNE to visualize the features extracted by the proposed network, further demonstrating the effectiveness of the feature extraction framework. We also conducted extensive ablation and hyperparameter tuning experiments to construct a robust network architecture that can be well generalized. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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6. A Spatio-Temporal Capsule Neural Network with Self-Correlation Routing for EEG Decoding of Semantic Concepts of Imagination and Perception Tasks.
- Author
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Huang, Jianxi, Chang, Yinghui, Li, Wenyu, Tong, Jigang, and Du, Shengzhi
- Subjects
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CAPSULE neural networks , *TRANSFORMER models , *COGNITIVE neuroscience , *BRAIN-computer interfaces , *SIGNAL-to-noise ratio , *FEATURE extraction , *ELECTROENCEPHALOGRAPHY - Abstract
Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Electroencephalogram (EEG) is commonly used in the relevant fields, because it is a low-cost noninvasive technique with high temporal resolution. However, as EEG signals contain a high noise level resulting in a low signal-to-noise ratio, it makes decoding EEG-based semantic concepts for imagination and perception tasks (SCIP-EEG) challenging. Currently, neural network algorithms such as CNN, RNN, and LSTM have almost reached their limits in EEG signal decoding due to their own short-comings. The emergence of transformer methods has improved the classification performance of neural networks for EEG signals. However, the transformer model has a large parameter set and high complexity, which is not conducive to the application of BCI. EEG signals have high spatial correlation. The relationship between signals from different electrodes is more complex. Capsule neural networks can effectively model the spatial relationship between electrodes through vector representation and a dynamic routing mechanism. Therefore, it achieves more accurate feature extraction and classification. This paper proposes a spatio-temporal capsule network with a self-correlation routing mechaninsm for the classification of semantic conceptual EEG signals. By improving the feature extraction and routing mechanism, the model is able to more effectively capture the highly variable spatio-temporal features from EEG signals and establish connections between capsules, thereby enhancing classification accuracy and model efficiency. The performance of the proposed model was validated using the publicly accessible semantic concept dataset for imagined and perceived tasks from Bath University. Our model achieved average accuracies of 94.9%, 93.3%, and 78.4% in the three sensory modalities (pictorial, orthographic, and audio), respectively. The overall average accuracy across the three sensory modalities is 88.9%. Compared to existing advanced algorithms, the proposed model achieved state-of-the-art performance, significantly improving classification accuracy. Additionally, the proposed model is more stable and efficient, making it a better decoding solution for SCIP-EEG decoding. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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7. 嵌入式系统中运动想象脑 - 机接口编解码算法综述.
- Author
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于钦雯, 周王成, 戴亚康, and 刘 燕
- Abstract
Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2024
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8. A multistrategy differential evolution algorithm combined with Latin hypercube sampling applied to a brain–computer interface to improve the effect of node displacement.
- Author
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Chang, Hanjui, Sun, Yue, Lu, Shuzhou, and Lin, Daiyao
- Subjects
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DIFFERENTIAL evolution , *LATIN hypercube sampling , *BRAIN-computer interfaces , *DISPLACEMENT (Psychology) , *INJECTION molding of plastics , *ELECTRONIC circuits - Abstract
Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain–computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain–computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain–computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain–computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain–computer interface after node displacement optimization can be evaluated. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
9. Translational Bioethics in China: Brain‐Computer Interface Research as a Case Study.
- Author
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Chen, Haidan
- Subjects
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HUMAN experimentation , *RESEARCH ethics , *TECHNOLOGICAL innovations , *TRANSLATIONAL research , *MEDICAL research - Abstract
The research and development of emerging technologies has potential long‐term and societal impacts that pose governance challenges. This essay summarizes the development of research ethics in China over the past few decades, as well as the measures taken by the Chinese government to build its ethical governance system of science and technology after the occurrence of the CRISPR‐babies incident. The essay then elaborates on the current problems of this system through the case study of ethical governance of brain‐computer interface research, and explores how the transition from research ethics to translational bioethics, which encourages interdisciplinary collaboration and focuses on societal implications, may respond to the challenges of ethical governance of science and technology. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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10. Enhancing Brain–Computer Interfaces through Kriging-Based Fusion of Sparse Regression Partial Differential Equations to Counter Injection Molding View of Node Displacement Effects.
- Author
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Chang, Hanjui, Sun, Yue, Lu, Shuzhou, and Lan, Yuntao
- Subjects
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PARTIAL differential equations , *ELECTRONIC equipment , *PLASTICS , *THIN films , *HEAT transfer , *INJECTION molding - Abstract
Highlights: IME technology is combined with conductive PET and LSR polymer materials to preform Utah arrays for BCI. The effects of three key factors on the nodal displacement were analyzed using the Kriging model. The heat transfer during injection molding was further analyzed by PDEs in order to determine the parameters more accurately. The allowable variation range of the wire diameter is obtained by the relation between the wire diameter and the current. Injection molding is an efficient and precise manufacturing technology that is widely used in the production of plastic products. In recent years, injection molding technology has made significant progress, especially with the combination of in-mold electronics (IME) technology, which makes it possible to embed electronic components directly into the surface of a product. IME technology improves the integration and performance of a product by embedding conductive materials and functional components in the mold. Brain–computer interfaces (BCIs) are a rapidly growing field of research that aims to capture, analyze, and feedback brain signals by directly connecting the brain to external devices. The Utah array, a high-density microelectrode array, has been widely used for the recording and transmission of brain signals. However, the traditional fabrication method of the Utah array suffers from high cost and low integration, which limits its promotion in practical applications. The lines that receive EEG signals are one of the key parts of a brain–computer interface system. The optimization of injection molding parameters is particularly important in order to effectively embed these lines into thin films and to ensure the precise displacement of the line nodes and the stability of signal transmission during the injection molding process. In this study, a method based on the Kriging prediction model and sparse regression partial differential equations (PDEs) is proposed to optimize the key parameters in the injection molding process. This method can effectively predict and control the displacement of nodes in the film, ensure the stability and reliability of the line during the injection process, and improve the accuracy of EEG signal transmission and system performance. The optimal injection parameters were finally obtained: a holding pressure of 525 MPa, a holding time of 50 s, and a melting temperature of 285 °C. Under this condition, the average node displacement of UA was reduced from the initial 0.19 mm to 0.89 µm, with an optimization rate of 95.32%. [ABSTRACT FROM AUTHOR]
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- 2024
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11. EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review.
- Author
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AlEssa, Ghada N. and Alzahrani, Saleh I.
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COLOR blindness ,EVOKED potentials (Electrophysiology) ,FEATURE extraction ,VISION disorders ,RESEARCH personnel - Abstract
Color vision deficiency (CVD) is one of the most common disorders related to visual impairment. Individuals with this condition are unable to differentiate between colors due to the absence or impairment of one or more color photoreceptors in their retinas. This disorder can be diagnosed through multiple approaches. This review paper provides a comprehensive summary of studies on applying Brain–Computer Interface (BCI) technology for diagnosing CVD. The main purpose of this review is to help researchers understand how BCI can be further developed and utilized for diagnosing CVD in the future. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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12. Evaluation of Different Visual Feedback Methods for Brain—Computer Interfaces (BCI) Based on Code-Modulated Visual Evoked Potentials (cVEP).
- Author
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Fodor, Milán András, Herschel, Hannah, Cantürk, Atilla, Heisenberg, Gernot, and Volosyak, Ivan
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VISUAL evoked potentials , *VISUAL perception , *KNOWLEDGE transfer , *USER experience , *ELECTROENCEPHALOGRAPHY , *BRAIN-computer interfaces - Abstract
Brain–computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals. BCIs based on code-modulated visual evoked potentials (cVEPs) are based on visual stimuli, thus appropriate visual feedback on the interface is crucial for an effective BCI system. Many previous studies have demonstrated that implementing visual feedback can improve information transfer rate (ITR) and reduce fatigue. This research compares a dynamic interface, where target boxes change their sizes based on detection certainty, with a threshold bar interface in a three-step cVEP speller. In this study, we found that both interfaces perform well, with slight variations in accuracy, ITR, and output characters per minute (OCM). Notably, some participants showed significant performance improvements with the dynamic interface and found it less distracting compared to the threshold bars. These results suggest that while average performance metrics are similar, the dynamic interface can provide significant benefits for certain users. This study underscores the potential for personalized interface choices to enhance BCI user experience and performance. By improving user friendliness, performance, and reducing distraction, dynamic visual feedback could optimize BCI technology for a broader range of users. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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13. Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain–Computer Interface System.
- Author
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Kabir, Md. Humaun, Akhtar, Nadim Ibne, Tasnim, Nishat, Miah, Abu Saleh Musa, Lee, Hyoun-Sup, Jang, Si-Woong, and Shin, Jungpil
- Subjects
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FISHER discriminant analysis , *MOTOR imagery (Cognition) , *MACHINE learning , *BRAIN-computer interfaces , *RESEARCH personnel , *FEATURE selection , *FEATURE extraction , *ELECTROENCEPHALOGRAPHY - Abstract
The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain–computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many researchers have been working to develop MI-based BCI recognition systems using various time-frequency feature extraction and classification approaches. However, the existing systems still face challenges in achieving satisfactory performance due to large amount of non-discriminative and ineffective features. To get around these problems, we suggested a multiband decomposition-based feature extraction and classification method that works well, along with a strong feature selection method for MI tasks. Our method starts by splitting the preprocessed EEG signal into four sub-bands. In each sub-band, we then used a common spatial pattern (CSP) technique to pull out narrowband-oriented useful features, which gives us a high-dimensional feature vector. Subsequently, we utilized an effective feature selection method, Relief-F, which reduces the dimensionality of the final features. Finally, incorporating advanced classification techniques, we classified the final reduced feature vector. To evaluate the proposed model, we used the three different EEG-based MI benchmark datasets, and our proposed model achieved better performance accuracy than existing systems. Our model's strong points include its ability to effectively reduce feature dimensionality and improve classification accuracy through advanced feature extraction and selection methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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14. Single-Trial Detection and Classification of Event-Related Optical Signals for a Brain–Computer Interface Application.
- Author
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Chiou, Nicole, Günal, Mehmet, Koyejo, Sanmi, Perpetuini, David, Chiarelli, Antonio Maria, Low, Kathy A., Fabiani, Monica, and Gratton, Gabriele
- Subjects
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BRAIN-computer interfaces , *CONVOLUTIONAL neural networks , *DEEP learning , *CLASSIFICATION , *DEEP brain stimulation , *REACTION time - Abstract
Event-related optical signals (EROS) measure fast modulations in the brain's optical properties related to neuronal activity. EROS offer a high spatial and temporal resolution and can be used for brain–computer interface (BCI) applications. However, the ability to classify single-trial EROS remains unexplored. This study evaluates the performance of neural network methods for single-trial classification of motor response-related EROS. EROS activity was obtained from a high-density recording montage covering the motor cortex during a two-choice reaction time task involving responses with the left or right hand. This study utilized a convolutional neural network (CNN) approach to extract spatiotemporal features from EROS data and perform classification of left and right motor responses. Subject-specific classifiers trained on EROS phase data outperformed those trained on intensity data, reaching an average single-trial classification accuracy of around 63%. Removing low-frequency noise from intensity data is critical for achieving discriminative classification results with this measure. Our results indicate that deep learning with high-spatial-resolution signals, such as EROS, can be successfully applied to single-trial classifications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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15. Simulation-Informed Power Budget Estimate of a Fully-Implantable Brain–Computer Interface.
- Author
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Serrano-Amenos, Claudia, Hu, Frank, Wang, Po T., Heydari, Payam, Do, An H., and Nenadic, Zoran
- Abstract
This study aims to estimate the maximum power consumption that guarantees a thermally safe operation for a titanium-enclosed chest wall unit (CWU) subcutaneously implanted in the pre-pectoral area. This unit is a central piece of an envisioned fully-implantable bi-directional brain–computer interface (BD-BCI). To this end, we created a thermal simulation model using the finite element method implemented in COMSOL. We also performed a sensitivity analysis to ensure that our predictions were robust against the natural variation of physiological and environmental parameters. Based on this analysis, we predict that the CWU can consume between 378 and 538 mW of power without raising the surrounding tissue's temperature above the thermal safety threshold of 2 ∘ C. This power budget should be sufficient to power all of the CWU's basic functionalities, which include training the decoder, online decoding, wireless data transmission, and cortical stimulation. This power budget assessment provides an important specification for the design of a CWU—an integral part of a fully-implantable BD-BCI system. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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16. Surface Electromyography-Based Recognition of Electronic Taste Sensations.
- Author
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Ullah, Asif, Zhang, Fengqi, Song, Zhendong, Wang, You, Zhao, Shuo, Riaz, Waqar, and Li, Guang
- Subjects
PLATINUM electrodes ,FEATURE extraction ,FACIAL muscles ,RANDOM forest algorithms ,SENSES - Abstract
Taste sensation recognition is a core for taste-related queries. Most prior research has been devoted to recognizing the basic taste sensations using the Brain–Computer Interface (BCI), which includes EEG, MEG, EMG, and fMRI. This research aims to recognize electronic taste (E-Taste) sensations based on surface electromyography (sEMG). Silver electrodes with platinum plating of the E-Taste device were placed on the tongue's tip to stimulate various tastes and flavors. In contrast, the electrodes of the sEMG were placed on facial muscles to collect the data. The dataset was organized and preprocessed, and a random forest classifier was applied, giving a five-fold accuracy of 70.43%. The random forest classifier was used on each participant dataset individually and in groups, providing the highest accuracy of 84.79% for a single participant. Moreover, various feature combinations were extracted and acquired 72.56% accuracy after extracting eight features. For a future perspective, this research offers guidance for electronic taste recognition based on sEMG. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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17. Electrotactile BCI for Top-Down Somatosensory Training: Clinical Feasibility Trial of Online BCI Control in Subacute Stroke Patients.
- Author
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Savić, Andrej M., Novičić, Marija, Miler-Jerković, Vera, Djordjević, Olivera, and Konstantinović, Ljubica
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ELECTRIC stimulation ,SELECTIVITY (Psychology) ,PERCEPTUAL learning ,EVOKED potentials (Electrophysiology) ,STROKE - Abstract
This study investigates the feasibility of a novel brain–computer interface (BCI) device designed for sensory training following stroke. The BCI system administers electrotactile stimuli to the user's forearm, mirroring classical sensory training interventions. Concurrently, selective attention tasks are employed to modulate electrophysiological brain responses (somatosensory event-related potentials—sERPs), reflecting cortical excitability in related sensorimotor areas. The BCI identifies attention-induced changes in the brain's reactions to stimulation in an online manner. The study protocol assesses the feasibility of online binary classification of selective attention focus in ten subacute stroke patients. Each experimental session includes a BCI training phase for data collection and classifier training, followed by a BCI test phase to evaluate online classification of selective tactile attention based on sERP. During online classification tests, patients complete 20 repetitions of selective attention tasks with feedback on attention focus recognition. Using a single electroencephalographic channel, attention classification accuracy ranges from 70% to 100% across all patients. The significance of this novel BCI paradigm lies in its ability to quantitatively measure selective tactile attention resources throughout the therapy session, introducing a top-down approach to classical sensory training interventions based on repeated neuromuscular electrical stimulation. [ABSTRACT FROM AUTHOR]
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- 2024
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18. Optimizing HMI for Intelligent Electric Vehicles Using BCI and Deep Neural Networks with Genetic Algorithms.
- Author
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Jin, Xinmin, Teng, Jian, and Lee, Shaw-mung
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ARTIFICIAL neural networks ,GENETIC algorithms ,TRAFFIC safety ,BRAIN-computer interfaces ,ELECTRIC vehicles - Abstract
This study utilizes a brain—computer interface (BCI)—based deep neural network (DNN) and genetic algorithm (GA) method. This research explores the interaction design of the main control human-machine interaction interfaces (HMIs) for intelligent electric vehicles (EVs) by integrating neural network predictions with genetic algorithm optimizations. Augmented reality (AR) was incorporated into the experimental setup to simulate real driving conditions, providing participants with an immersive and realistic experience. A comparative analysis of several models including the support vector machines-genetic algorithm (SVMs-GA), decision trees-genetic algorithm (DT-GA), particle swarm optimization-genetic algorithm (PSO-GA), and deep neural network-genetic algorithm (DNN-GA) was conducted. The results indicate that the DNN-GA model exhibited superior prediction accuracy with the lowest mean squared error (MSE) of 0.22 and mean absolute error (MAE) of 0.31. Additionally, the DNN-GA model demonstrated the shortest training time of 69.93 s, making it 4.5% more efficient than the PSO-GA model and 51.8% more efficient compared to the SVMs-GA model. This research focuses on promoting an innovative and efficient machine learning hybrid model with the goal of improving the efficiency of the human-machine interaction interfaces (HMIs) interface of intelligent electric vehicles. By optimizing the accuracy and response speed, the aim is to enhance the control interface and significantly improve user experience and usability. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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19. Joint hybrid recursive feature elimination based channel selection and ResGCN for cross session MI recognition
- Author
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Duan Li, Keyun Li, Yongquan Xia, Jianhua Dong, and Ronglei Lu
- Subjects
Brain-computer interface (BCI) ,Motor imagery (MI) ,Channel selection ,Deep learning ,Graph convolutional neural network (GCN) ,Medicine ,Science - Abstract
Abstract In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively.
- Published
- 2024
- Full Text
- View/download PDF
20. A multistrategy differential evolution algorithm combined with Latin hypercube sampling applied to a brain–computer interface to improve the effect of node displacement
- Author
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Hanjui Chang, Yue Sun, Shuzhou Lu, and Daiyao Lin
- Subjects
Brain–computer interface (BCI) ,In-mold electronic decoration (IME) ,Multistrategy differential evolution (MSDE) algorithm ,Latin hypercube sampling (LHS) ,Entropy value ,Node displacement ,Medicine ,Science - Abstract
Abstract Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain–computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain–computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain–computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain–computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain–computer interface after node displacement optimization can be evaluated.
- Published
- 2024
- Full Text
- View/download PDF
21. CTNet: a convolutional transformer network for EEG-based motor imagery classification
- Author
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Wei Zhao, Xiaolu Jiang, Baocan Zhang, Shixiao Xiao, and Sujun Weng
- Subjects
Brain-computer interface (BCI) ,Motor imagery (MI) ,Transformer ,Convolutional neural networks (CNN) ,Medicine ,Science - Abstract
Abstract Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling the translation of thought into actionable commands for interactive and assistive technologies. However, the constrained decoding performance of brain signals poses a limitation to the broader application and development of BCI systems. In this study, we introduce a convolutional Transformer network (CTNet) designed for EEG-based MI classification. Firstly, CTNet employs a convolutional module analogous to EEGNet, dedicated to extracting local and spatial features from EEG time series. Subsequently, it incorporates a Transformer encoder module, leveraging a multi-head attention mechanism to discern the global dependencies of EEG's high-level features. Finally, a straightforward classifier module comprising fully connected layers is followed to categorize EEG signals. In subject-specific evaluations, CTNet achieved remarkable decoding accuracies of 82.52% and 88.49% on the BCI IV-2a and IV-2b datasets, respectively. Furthermore, in the challenging cross-subject assessments, CTNet achieved recognition accuracies of 58.64% on the BCI IV-2a dataset and 76.27% on the BCI IV-2b dataset. In both subject-specific and cross-subject evaluations, CTNet holds a leading position when compared to some of the state-of-the-art methods. This underscores the exceptional efficacy of our approach and its potential to set a new benchmark in EEG decoding.
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- 2024
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22. Filter bank temporally local multivariate synchronization index for SSVEP-based BCI
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Tingting Xu, Zhuojie Ji, Xin Xu, and Lei Wang
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Brain–computer interface (BCI) ,Filter bank ,Multivariate synchronization index (MSI) ,Steady-state visual evoked potential (SSVEP) ,Temporal local information ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background Multivariate synchronization index (MSI) has been successfully applied for frequency detection in steady state visual evoked potential (SSVEP) based brain–computer interface (BCI) systems. However, the standard MSI algorithm and its variants cannot simultaneously take full advantage of the time-local structure and the harmonic components in SSVEP signals, which are both crucial for frequency detection performance. To overcome the limitation, we propose a novel filter bank temporally local MSI (FBTMSI) algorithm to further improve SSVEP frequency detection accuracy. The method explicitly utilizes the temporal information of signal for covariance matrix estimation and employs filter bank decomposition to exploits SSVEP-related harmonic components. Results We employed the cross-validation strategy on the public Benchmark dataset to optimize the parameters and evaluate the performance of the FBTMSI algorithm. Experimental results show that FBTMSI outperforms the standard MSI, temporally local MSI (TMSI) and filter bank driven MSI (FBMSI) algorithms across multiple experimental settings. In the case of data length of one second, the average accuracy of FBTMSI is 9.85% and 3.15% higher than that of the FBMSI and the TMSI, respectively. Conclusions The promising results demonstrate the effectiveness of the FBTMSI algorithm for frequency recognition and show its potential in SSVEP-based BCI applications.
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- 2024
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23. Critical Thinking in the Age of Expanded Telepathy and Brain-Computer Interface (BCI)
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Luciano Zubillaga
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telepathy ,brain-computer interface (bci) ,cosmism ,gurdjieff ,entanglement ,direct theory ,metahuman ,Ethnology. Social and cultural anthropology ,GN301-674 - Abstract
Unveiling a world of expanded telepathy, the study investigates the convergence of cosmological awareness and brain-computer interface (BCI) technologies. It delves into the evolution or potential disappearance of individual critical thinking in a posthuman era, exploring the emergence of a new form of criticality beyond human boundaries. By envisioning a future where telepathy becomes a reality, the paper examines the direct sharing of mental states, bypassing traditional communication methods and raising profound questions about the nature of consciousness and intentionality.
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- 2024
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24. A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques
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Md. Fazlul Karim Khondakar, Md. Hasib Sarowar, Mehdi Hasan Chowdhury, Sumit Majumder, Md. Azad Hossain, M. Ali Akber Dewan, and Quazi Delwar Hossain
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Neuromarketing ,Brain-computer interface (BCI) ,Electroencephalography (EEG) ,Research trend ,Pre-processing ,Feature ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Computer software ,QA76.75-76.765 - Abstract
Abstract Neuromarketing is an emerging research field that aims to understand consumers’ decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future.
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- 2024
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25. Beginning of AI+BCI silicon-based carbon-based fusion new intelligence
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YIN Kuiying, YU Tao
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artificial intelligence (ai) ,brain-computer interface (bci) ,human brain visual representation ,brain vision reconstruction ,consciousness twin ,Military Science - Abstract
We are embracing the fourth wave of human development, which is a critical transition from the information society to an intelligent society integrating human beings, the physical world, and the cyberspace. In recent years, computing and information technology have developed rapidly. The unprecedented popularity and success of deep learning have established artificial intelligence (AI) as the frontier field of human exploration of machine intelligence. Meanwhile, thanks to the revolutionary progress of devices and the development of artificial intelligence (AI), brain-computer interface (BCI) implantation technology has also been rapidly implemented, which marks the beginning of the integration of BCI and AI, carbon-based and silicon-based. However, there are fundamental differences between the underlying logic of silicon-based and carbon-based computing, and the intelligent mechanism of the brain remains to be further explored. The visual cognition-guided twin AI deep network proposed in this study is a deep network technology driven by personal consciousness. It captures and analyzes individual thinking patterns and creative inspiration to create a unique visual world tailored for each user. In such an environment, everyone becomes the visual leader of their own created world, breaking the barriers between matter and consciousness, and expressing rich individuality and creativity.
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- 2024
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26. Motor imagery-based brain–computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients
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Zhen-Zhen Ma, Jia-Jia Wu, Zhi Cao, Xu-Yun Hua, Mou-Xiong Zheng, Xiang-Xin Xing, Jie Ma, and Jian-Guang Xu
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Brain–computer interface (BCI) ,Fugl–Meyer Assessment of the Upper Extremity (FMA-UE) ,Motor imagery (MI) ,Stroke rehabilitation ,fMRI ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Abstract Background The most challenging aspect of rehabilitation is the repurposing of residual functional plasticity in stroke patients. To achieve this, numerous plasticity-based clinical rehabilitation programs have been developed. This study aimed to investigate the effects of motor imagery (MI)-based brain–computer interface (BCI) rehabilitation programs on upper extremity hand function in patients with chronic hemiplegia. Design A 2010 Consolidated Standards for Test Reports (CONSORT)-compliant randomized controlled trial. Methods Forty-six eligible stroke patients with upper limb motor dysfunction participated in the study, six of whom dropped out. The patients were randomly divided into a BCI group and a control group. The BCI group received BCI therapy and conventional rehabilitation therapy, while the control group received conventional rehabilitation only. The Fugl–Meyer Assessment of the Upper Extremity (FMA-UE) score was used as the primary outcome to evaluate upper extremity motor function. Additionally, functional magnetic resonance imaging (fMRI) scans were performed on all patients before and after treatment, in both the resting and task states. We measured the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), z conversion of ALFF (zALFF), and z conversion of ReHo (ReHo) in the resting state. The task state was divided into four tasks: left-hand grasping, right-hand grasping, imagining left-hand grasping, and imagining right-hand grasping. Finally, meaningful differences were assessed using correlation analysis of the clinical assessments and functional measures. Results A total of 40 patients completed the study, 20 in the BCI group and 20 in the control group. Task-related blood-oxygen-level-dependent (BOLD) analysis showed that when performing the motor grasping task with the affected hand, the BCI group exhibited significant activation in the ipsilateral middle cingulate gyrus, precuneus, inferior parietal gyrus, postcentral gyrus, middle frontal gyrus, superior temporal gyrus, and contralateral middle cingulate gyrus. When imagining a grasping task with the affected hand, the BCI group exhibited greater activation in the ipsilateral superior frontal gyrus (medial) and middle frontal gyrus after treatment. However, the activation of the contralateral superior frontal gyrus decreased in the BCI group relative to the control group. Resting-state fMRI revealed increased zALFF in multiple cerebral regions, including the contralateral precentral gyrus and calcarine and the ipsilateral middle occipital gyrus and cuneus, and decreased zALFF in the ipsilateral superior temporal gyrus in the BCI group relative to the control group. Increased zReHo in the ipsilateral cuneus and contralateral calcarine and decreased zReHo in the contralateral middle temporal gyrus, temporal pole, and superior temporal gyrus were observed post-intervention. According to the subsequent correlation analysis, the increase in the FMA-UE score showed a positive correlation with the mean zALFF of the contralateral precentral gyrus (r = 0.425, P
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- 2024
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27. AttentionCARE: replicability of a BCI for the clinical application of augmented reality-guided EEG-based attention modification for adolescents at high risk for depression.
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Gall, Richard, Mcdonald, Nastasia, Xiaofei Huang, Wears, Anna, Price, Rebecca B., Ostadabbas, Sarah, Akcakaya, Murat, and Woody, Mary L.
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CLINICAL medicine ,EMOTIONAL conditioning ,TEENAGERS ,BRAIN-computer interfaces ,MENTAL depression ,EYE tracking - Abstract
Affect-biased attention is the phenomenon of prioritizing attention to emotionally salient stimuli and away fromgoal-directed stimuli. It is thought that affect-biased attention to emotional stimuli is a driving factor in the development of depression. This effect has been well-studied in adults, but research shows that this is also true during adolescence, when the severity of depressive symptoms are correlated with the magnitude of affect-biased attention to negative emotional stimuli. Prior studies have shown that trainings to modify affect-biased attention may ameliorate depression in adults, but this research has also been stymied by concerns about reliability and replicability. This study describes a clinical application of augmented reality-guided EEG-based attention modification ("AttentionCARE") for adolescents who are at highest risk for future depressive disorders (i.e., daughters of depressed mothers). Our results (n = 10) indicated that the AttentionCARE protocol can reliably and accurately provide neurofeedback about adolescent attention to negative emotional distractors that detract from attention to a primary task. Through several within and cross-study replications, our work addresses concerns about the lack of reliability and reproducibility in brain-computer interface applications, offering insights for future interventions to modify affect-biased attention in high-risk adolescents. [ABSTRACT FROM AUTHOR]
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- 2024
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28. Trial of Brain–Computer Interface for Continuous Motion Using Electroencephalography and Electromyography.
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Saga, Norihiko, Okawa, Yukina, Saga, Takuma, Satoh, Toshiyuki, and Saito, Naoki
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FUZZY logic ,ELECTROMYOGRAPHY ,ELECTROENCEPHALOGRAPHY ,NEUROREHABILITATION ,FUZZY systems - Abstract
Most BCI systems used in neurorehabilitation detect EEG features indicating motor intent based on machine learning, focusing on repetitive movements, such as limb flexion and extension. These machine learning methods require large datasets and are time consuming, making them unsuitable for same-day rehabilitation training following EEG measurements. Therefore, we propose a BMI system based on fuzzy inference that bypasses the need for specific EEG features, introducing an algorithm that allows patients to progress from measurement to training within a few hours. Additionally, we explored the integration of electromyography (EMG) with conventional EEG-based motor intention estimation to capture continuous movements, which is essential for advanced motor function training, such as skill improvement. In this study, we developed an algorithm that detects the initial movement via EEG and switches to EMG for subsequent movements. This approach ensures real-time responsiveness and effective handling of continuous movements. Herein, we report the results of this study. [ABSTRACT FROM AUTHOR]
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- 2024
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29. A Comprehensive Review on Brain–Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation.
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Elashmawi, Walaa H., Ayman, Abdelrahman, Antoun, Mina, Mohamed, Habiba, Mohamed, Shehab Eldeen, Amr, Habiba, Talaat, Youssef, and Ali, Ahmed
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MACHINE learning ,REHABILITATION technology ,LITERATURE reviews ,STROKE rehabilitation ,MOTOR ability - Abstract
This literature review explores the pivotal role of brain–computer interface (BCI) technology, coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for individuals with damaged muscles and motor systems. This study provides a comprehensive overview of recent developments in BCI and motor control for rehabilitation, emphasizing the integration of user-friendly technological support and robotic prosthetics powered by brain activity. This review critically examines the latest strides in BCI technology and its application in motor skill recovery. Special attention is given to prevalent EEG devices adaptable for BCI-driven rehabilitation. The study surveys significant contributions in the realm of machine learning-based and deep learning-based rehabilitation evaluation. The integration of BCI with EEG technology demonstrates promising outcomes for enhancing motor skills in rehabilitation. The study identifies key EEG devices suitable for BCI applications, discusses advancements in machine learning approaches for rehabilitation assessment, and highlights the emergence of novel robotic prosthetics powered by brain activity. Furthermore, it showcases successful case studies illustrating the practical implementation of BCI-driven rehabilitation techniques and their positive impact on diverse patient populations. This review serves as a cornerstone for informed decision-making in the field of BCI technology for rehabilitation. The results highlight BCI's diverse advantages, enhancing motor control and robotic integration. The findings highlight the potential of BCI in reshaping rehabilitation practices and offer insights and recommendations for future research directions. This study contributes significantly to the ongoing transformation of BCI technology, particularly through the utilization of EEG equipment, providing a roadmap for researchers in this dynamic domain. [ABSTRACT FROM AUTHOR]
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- 2024
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30. Filter bank temporally local multivariate synchronization index for SSVEP-based BCI.
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Xu, Tingting, Ji, Zhuojie, Xu, Xin, and Wang, Lei
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- *
FILTER banks , *VISUAL evoked potentials , *COMMUNITY banks , *BRAIN-computer interfaces , *SYNCHRONIZATION , *GABOR filters , *KALMAN filtering - Abstract
Background: Multivariate synchronization index (MSI) has been successfully applied for frequency detection in steady state visual evoked potential (SSVEP) based brain–computer interface (BCI) systems. However, the standard MSI algorithm and its variants cannot simultaneously take full advantage of the time-local structure and the harmonic components in SSVEP signals, which are both crucial for frequency detection performance. To overcome the limitation, we propose a novel filter bank temporally local MSI (FBTMSI) algorithm to further improve SSVEP frequency detection accuracy. The method explicitly utilizes the temporal information of signal for covariance matrix estimation and employs filter bank decomposition to exploits SSVEP-related harmonic components. Results: We employed the cross-validation strategy on the public Benchmark dataset to optimize the parameters and evaluate the performance of the FBTMSI algorithm. Experimental results show that FBTMSI outperforms the standard MSI, temporally local MSI (TMSI) and filter bank driven MSI (FBMSI) algorithms across multiple experimental settings. In the case of data length of one second, the average accuracy of FBTMSI is 9.85% and 3.15% higher than that of the FBMSI and the TMSI, respectively. Conclusions: The promising results demonstrate the effectiveness of the FBTMSI algorithm for frequency recognition and show its potential in SSVEP-based BCI applications. [ABSTRACT FROM AUTHOR]
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- 2024
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31. Hybrid approach: combining eCCA and SSCOR for enhancing SSVEP decoding.
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Hamou, Soukaina, Moufassih, Mustapha, Tarahi, Ousama, Agounad, Said, and Azami, Hafida Idrissi
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VISUAL evoked potentials , *FILTER banks , *SPATIAL filters , *STATISTICAL correlation , *STATISTICAL significance - Abstract
Currently, steady-state visual evoked potentials (SSVEPs) are applied in a variety of fields. In these applications, spatial filtering is the most commonly used method for decoding SSVEPs. However, existing methods for the SSVEP decoding require improvements in terms of the interaction speed, accuracy, and ease of use. Recent advances in performance have been attributed to the existing hybridisation-based approaches. This article presents and evaluates a novel approach for target identification of SSVEP. The suggested target recognition method significantly enhances the performance of SSVEP decoding by combining two existing methods: extended canonical correlation analysis (eCCA) and the sum of squared correlation (SSCOR). This combined approach is referred to as the extended CCA and sum of squared correlation approach (eCCA-SSCOR). The results of the offline experimental comparison are based on two publicly available datasets, including the San Diego dataset, which comprises 12 frequency targets recorded from 10 individuals, and the Benchmark dataset, comprising 40 frequency targets from 35 subjects. The statistical significance of the improvement was tested using paired samples t-tests and Wilcoxon rank-sum tests. The results indicate that the suggested eCCA-SSCOR approach significantly improves detection accuracy and ITR compared to four existing state-of-the-art methods: canonical correlation analysis (CCA), filter bank CCA (FBCCA), eCCA, and SSCOR. Our method achieved a high average accuracy for all subjects, with 99.5% for the San Diego dataset, and 98.60% for the Benchmark dataset corresponding to data length of 3.5 s and 5 s, respectively. Furthermore, a high ITR of 212.44 bits/min was achieved with a data duration ( T w ) of 0.75 s using the San Diego dataset and was 238.66 bits/min with a T w of 1 s for the Benchmark dataset. These offline results demonstrate that the proposed approach successfully combines eCCA and SSCOR to improve SSVEP decoding. Additionally, it is advantageous for applications based on offline processing and can be adapted for online analysis. Online applications can benefit from these results by providing fast interfaces with a large number of commands. [ABSTRACT FROM AUTHOR]
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- 2024
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32. Recording the tactile P300 with the cEEGrid for potential use in a brain-computer interface.
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Eidel, M., Pfeiffer, M., Ziebell, P., and Kübler, A.
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BRAIN-computer interfaces ,VIBROTACTILE stimulation ,ASSISTIVE technology ,EVOKED potentials (Electrophysiology) ,ELECTROENCEPHALOGRAPHY ,EAR - Abstract
Brain-computer interfaces (BCIs) are scientifically well established, but they rarely arrive in the daily lives of potential end-users. This could be in part because electroencephalography (EEG), a prevalent method to acquire brain activity for BCI operation, is considered too impractical to be applied in daily life of endusers with physical impairment as an assistive device. Hence, miniaturized EEG systems such as the cEEGrid have been developed. While they promise to be a step toward bridging the gap between BCI development, lab demonstrations, and home use, they still require further validation. Encouragingly, the cEEGrid has already demonstrated its ability to record visually and auditorily evoked eventrelated potentials (ERP), which are important as input signal for many BCIs. With this study, we aimed at evaluating the cEEGrid in the context of a BCI based on tactually evoked ERPs. To compare the cEEGrid with a conventional scalp EEG, we recorded brain activity with both systems simultaneously. Forty healthy participants were recruited to perform a P300 oddball task based on vibrotactile stimulation at four different positions. This tactile paradigm has been shown to be feasible for BCI repeatedly but has never been tested with the cEEGrid. We found distinct P300 deflections in the cEEGrid data, particularly at vertical bipolar channels. With an average of 63%, the cEEGrid classification accuracy was significantly above the chance level (25%) but significantly lower than the 81% reached with the EEG cap. Likewise, the P300 amplitude was significantly lower (cEEGrid R2-R7: 1.87 µV, Cap Cz: 3.53 µV). These results indicate that a tactile BCI using the cEEGrid could potentially be operated, albeit with lower efficiency. Additionally, participants' somatosensory sensitivity was assessed, but no correlation to the accuracy of either EEG system was shown. Our research contributes to the growing amount of literature comparing the cEEGrid to conventional EEG systems and provides first evidence that the tactile P300 can be recorded behind the ear. A BCI based on a thus simplified EEG system might be more readily accepted by potential end-users, provided the accuracy can be substantially increased, e.g., by training and improved classification. [ABSTRACT FROM AUTHOR]
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- 2024
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33. Post-stroke aphasia rehabilitation using an adapted visual P300 brain-computer interface training: improvement over time, but specificity remains undetermined.
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Kleih, Sonja C. and Botrel, Loic
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BRAIN-computer interfaces ,APHASIA ,REHABILITATION ,STROKE ,QUALITY of life ,LOW vision - Abstract
Introduction: This study aimed to evaluate the efficacy of visual P300 braincomputer interface use to support rehabilitation of chronic language production deficits commonly experienced by individuals with a left-sided stroke resulting in post-stroke aphasia. Methods: The study involved twelve participants, but five dropped out. Additionally, data points were missing for three participants in the remaining sample of seven participants. The participants underwent four assessments--a baseline, preassessment, post-assessment, and follow-up assessment. Between the pre-and post-assessment, the participants underwent at least 14 sessions of visual spelling using a brain-computer interface. The study aimed to investigate the impact of this intervention on attention, language production, and language comprehension and to determine whether there were any potential effects on quality of life and well-being. Results: None of the participants showed a consistent improvement in attention. All participants showed an improvement in spontaneous speech production, and three participants experienced a reduction in aphasia severity. We found an improvement in subjective quality of life and daily functioning. However, we cannot rule out the possibility of unspecific effects causing or at least contributing to these results. Conclusion: Due to challenges in assessing the patient population, resulting in a small sample size and missing data points, the results of using visual P300 brain-computer interfaces for chronic post-stroke aphasia rehabilitation are preliminary. Thus, we cannot decisively judge the potential of this approach. [ABSTRACT FROM AUTHOR]
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- 2024
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34. A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques.
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Khondakar, Md. Fazlul Karim, Sarowar, Md. Hasib, Chowdhury, Mehdi Hasan, Majumder, Sumit, Hossain, Md. Azad, Dewan, M. Ali Akber, and Hossain, Quazi Delwar
- Subjects
NEUROMARKETING ,CONSUMER behavior ,PURCHASING ,MARKETING ,MARKETING strategy ,ELICITATION technique - Abstract
Neuromarketing is an emerging research field that aims to understand consumers' decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
35. Limited value of EEG source imaging for decoding hand movement and imagery in youth with brain lesions.
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Leung, Jason, Akter, Masuma, and Chau, Tom
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MAGNETIC resonance imaging , *BRAIN damage , *MOTOR imagery (Cognition) , *FEATURE extraction , *WHITE matter (Nerve tissue) - Abstract
Brain–computer interfaces based on electroencephalography (EEG) often exhibit unreliable performance in the classification of motor tasks. Recent research has shown that EEG source imaging (ESI) has the potential to outperform sensor domain approaches in various movement decoding tasks. However, ESI research to date has predominantly focused on the adult population, so its performance in youth with disabilities is unknown. In this study, we compared the offline classification performance of two ESI approaches (with and without modeling white matter conductivity anisotropy) to that of a sensor domain approach in the classification of left- versus right-hand movement execution and imagery tasks. Magnetic resonance images (MRI) were acquired from nine pediatric participants with brain lesions. Subsequently, cortical activity was recorded from 64 channels. MRI data were used to estimate participant-specific EEG sources. Various feature extraction and classification approaches were investigated in both sensor and source domains. Generally, ESI classification performance did not exceed chance levels and was statistically equivalent to sensor approaches except for isolated participants. However, ESI offered +9.61% improvement over the sensor domain (p = 0.031) in decoding motor execution in a participant with unilateral ventriculomegaly. Future research ought to delineate the specific task and participant characteristics which warrant the source domain approach. [ABSTRACT FROM AUTHOR]
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- 2024
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36. Genetic algorithm based deep learning model adaptation for improvising the motor imagery classification.
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R, Vishnupriya, Robinson, Neethu, and M, Ramasubba Reddy
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- *
CONVOLUTIONAL neural networks , *BRAIN-computer interfaces , *MOTOR imagery (Cognition) , *DEEP learning , *GENETIC algorithms - Abstract
Deep learning methods have proved a promising performance for electroencephalography-based brain-computer interfaces (EEG-BCI). It is particularly encouraging that a subject-independent model can be trained using a large amount of other subjects' data. Transfer learning methods such as adaptation or fine-tuning can be used on the pre-trained model to improve the performance. This study examined the influence of fine-tuning on the subject-independent model for EEG-based motor imagery (MI) classification using a genetic algorithm (GA). The proposed method is evaluated on the binary class MI dataset from the Korea University EEG dataset. Results show that the proposed GA-based fine-tuning approach statistically improved the average classification accuracy of the baseline model from 84.46% to 87.29%. More interestingly, our approach shows significant improvement in cases where the performance of the baseline model is poor after fine-tuning using other approaches. Further, layer-wise relevance propagation (LRP) is used to analyze the adapted models to gain a deeper understanding of the neurophysiological explanations underlying the model's decision. [ABSTRACT FROM AUTHOR]
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- 2024
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37. Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces.
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von Groll, Valentina Gamboa, Leeuwis, Nikki, Rimbert, Sébastien, Roc, Aline, Pillette, Léa, Lotte, Fabien, and Alimardani, Maryam
- Subjects
- *
MOTOR imagery (Cognition) , *BRAIN-computer interfaces , *SAMPLE size (Statistics) , *GENDER , *RHYTHM - Abstract
The utmost issue in Motor Imagery Brain-Computer Interfaces (MI-BCI) is the BCI poor performance known as 'BCI inefficiency'. Although past research has attempted to find a solution by investigating factors influencing users' MI-BCI performance, the issue persists. One of the factors that has been studied in relation to MI-BCI performance is gender. Research regarding the influence of gender on a user's ability to control MI-BCIs remains inconclusive, mainly due to the small sample size and unbalanced gender distribution in past studies. To address these issues and obtain reliable results, this study combined four MI-BCI datasets into one large dataset with 248 subjects and equal gender distribution. The datasets included EEG signals from healthy subjects from both gender groups who had executed a right- vs. left-hand motor imagery task following the Graz protocol. The analysis consisted of extracting the Mu Suppression Index from C3 and C4 electrodes and comparing the values between female and male participants. Unlike some of the previous findings which reported an advantage for female BCI users in modulating mu rhythm activity, our results did not show any significant difference between the Mu Suppression Index of both groups, indicating that gender may not be a predictive factor for BCI performance. [ABSTRACT FROM AUTHOR]
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- 2024
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38. Evaluation of Different Types of Stimuli in an Event-Related Potential-Based Brain–Computer Interface Speller under Rapid Serial Visual Presentation.
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Ron-Angevin, Ricardo, Fernández-Rodríguez, Álvaro, Velasco-Álvarez, Francisco, Lespinet-Najib, Véronique, and André, Jean-Marc
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- *
GAZE , *BRAIN-computer interfaces , *VISUAL perception , *EVOKED potentials (Electrophysiology) , *EYE movements , *EYE - Abstract
Rapid serial visual presentation (RSVP) is currently a suitable gaze-independent paradigm for controlling visual brain–computer interfaces (BCIs) based on event-related potentials (ERPs), especially for users with limited eye movement control. However, unlike gaze-dependent paradigms, gaze-independent ones have received less attention concerning the specific choice of visual stimuli that are used. In gaze-dependent BCIs, images of faces—particularly those tinted red—have been shown to be effective stimuli. This study aims to evaluate whether the colour of faces used as visual stimuli influences ERP-BCI performance under RSVP. Fifteen participants tested four conditions that varied only in the visual stimulus used: grey letters (GL), red famous faces with letters (RFF), green famous faces with letters (GFF), and blue famous faces with letters (BFF). The results indicated significant accuracy differences only between the GL and GFF conditions, unlike prior gaze-dependent studies. Additionally, GL achieved higher comfort ratings compared with other face-related conditions. This study highlights that the choice of stimulus type impacts both performance and user comfort, suggesting implications for future ERP-BCI designs for users requiring gaze-independent systems. [ABSTRACT FROM AUTHOR]
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- 2024
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39. Three-stage transfer learning for motor imagery EEG recognition.
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Li, Junhao, She, Qingshan, Meng, Ming, Du, Shengzhi, and Zhang, Yingchun
- Abstract
Motor imagery (MI) paradigms have been widely used in neural rehabilitation and drowsiness state assessment. The progress in brain-computer interface (BCI) technology has emphasized the importance of accurately and efficiently detecting motor imagery intentions from electroencephalogram (EEG). Despite the recent breakthroughs made in developing EEG-based algorithms for decoding MI, the accuracy and efficiency of these models remain limited by technical challenges posed by cross-subject heterogeneity in EEG data processing and the scarcity of EEG data for training. Inspired by the optimal transport theory, this study aims to develop a novel three-stage transfer learning (TSTL) method, which uses the existing labeled data from a source domain to improve classification performance on an unlabeled target domain. Notably, the proposed method comprises three components, namely, the Riemannian tangent space mapping (RTSM), source domain transformer (SDT), and optimal subspace mapping (OSM). The RTSM maps a symmetric positive definite matrix from the Riemannian space to the tangent space to minimize the marginal probability distribution drift. The SDT transforms the source domain to a target domain by finding the optimal transport mapping matrix to reduce the joint probability distribution differences. The OSM finally maps the transformed source domain and original target domain to the same subspace to further mitigate the distribution discrepancy. The performance of the proposed method was validated on two public BCI datasets, and the average accuracy of the algorithm on two datasets was 72.24% and 69.29%. Our results demonstrated the improved performance of EEG-based MI detection in comparison with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]
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- 2024
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40. AI+ BCI 硅基碳基融合新智能的开始.
- Author
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尹奎英 and 遇 涛
- Abstract
We are embracing the fourth wave of human development, which is a critical transition from the information society to an intelligent society integrating human beings, the physical world, and the cyberspace. In recent years, computing and information technology have developed rapidly. The unprecedented popularity and success of deep learning have established artificial intelligence (AI) as the frontier field of human exploration of machine intelligence. Meanwhile, thanks to the revolutionary progress of devices and the development of artificial intelligence (AI), brain-computer interface (BCI) implantation technology has also been rapidly implemented, which marks the beginning of the integration of BCI and AI, carbon-based and silicon-based. However, there are fundamental differences between the underlying logic of silicon-based and carbon-based computing, and the intelligent mechanism of the brain remains to be further explored. The visual cognition-guided twin AI deep network proposed in this study is a deep network technology driven by personal consciousness. It captures and analyzes individual thinking patterns and creative inspiration to create a unique visual world tailored for each user. In such an environment, everyone becomes the visual leader of their own created world, breaking the barriers between matter and consciousness, and expressing rich individuality and creativity. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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41. A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG.
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Li, Xiaodong, Yang, Shuoheng, Fei, Ningbo, Wang, Junlin, Huang, Wei, and Hu, Yong
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- *
CONVOLUTIONAL neural networks , *VISUAL evoked potentials , *BRAIN-computer interfaces , *ELECTROENCEPHALOGRAPHY - Abstract
The application of wearable electroencephalogram (EEG) devices is growing in brain–computer interfaces (BCI) owing to their good wearability and portability. Compared with conventional devices, wearable devices typically support fewer EEG channels. Devices with few-channel EEGs have been proven to be available for steady-state visual evoked potential (SSVEP)-based BCI. However, fewer-channel EEGs can cause the BCI performance to decrease. To address this issue, an attention-based complex spectrum–convolutional neural network (atten-CCNN) is proposed in this study, which combines a CNN with a squeeze-and-excitation block and uses the spectrum of the EEG signal as the input. The proposed model was assessed on a wearable 40-class dataset and a public 12-class dataset under subject-independent and subject-dependent conditions. The results show that whether using a three-channel EEG or single-channel EEG for SSVEP identification, atten-CCNN outperformed the baseline models, indicating that the new model can effectively enhance the performance of SSVEP-BCI with few-channel EEGs. Therefore, this SSVEP identification algorithm based on a few-channel EEG is particularly suitable for use with wearable EEG devices. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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42. A novel precisely designed compact convolutional EEG classifier for motor imagery classification.
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Abbasi, Muhammad Ahmed, Abbasi, Hafza Faiza, Aziz, Muhammad Zulkifal, Haider, Waseem, Fan, Zeming, and Yu, Xiaojun
- Abstract
Robust classification of electroencephalogram data for motor imagery recognition is of paramount importance in brain–computer interface (BCI) domain. Since EEG signals are highly subject-dependent, inter-subject variations can greatly impair the robustness of motor imagery (MI) classification. Therefore, this study introduces a precisely designed deep learning architecture namely compact convolutional EEG classifier (CCEC) which achieves better performance in both precision and efficiency. Specifically, the recorded EEG signals are first denoised using multiscale principal component analysis (MSPCA) technique. Then, such raw EEG data are converted into small tempo-spatial data matrices with a two-step signal preprocessing technique. Finally, the tempo-spatial matrices are fed to the proposed CCEC model for MI classification. Experimental results on two benchmark datasets demonstrate that the proposed model not only performs exceptionally well in subject-specific case with an average classification accuracy of 98.2% on dataset 1 but also shows a reasonable average classification accuracy of 72.64% in the subject-independent case. Additionally, with a mere 10% adaptation to subject-specific data, a further improvement of 18% is achieved, thus attaining a noteworthy 90% accuracy in the inter-subject classification. Results also reveal that the proposed CCEC model is highly robust to noisy data, ensuring reliable performance in real-world scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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43. MST-DGCN: A Multi-Scale Spatio-Temporal and Dynamic Graph Convolution Fusion Network for Electroencephalogram Recognition of Motor Imagery.
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Chen, Yuanling, Liu, Peisen, and Li, Duan
- Subjects
MOTOR imagery (Cognition) ,GRAPH neural networks ,DEEP learning ,ELECTROENCEPHALOGRAPHY ,DATA augmentation ,BRAIN-computer interfaces ,FEATURE extraction - Abstract
The motor imagery brain-computer interface (MI-BCI) has the ability to use electroencephalogram (EEG) signals to control and communicate with external devices. By leveraging the unique characteristics of task-related brain signals, this system facilitates enhanced communication with these devices. Such capabilities hold significant potential for advancing rehabilitation and the development of assistive technologies. In recent years, deep learning has received considerable attention in the MI-BCI field due to its powerful feature extraction and classification capabilities. However, two factors significantly impact the performance of deep-learning models. The size of the EEG datasets influences how effectively these models can learn. Similarly, the ability of classification models to extract features directly affects their accuracy in recognizing patterns. In this paper, we propose a Multi-Scale Spatio-Temporal and Dynamic Graph Convolution Fusion Network (MST-DGCN) to address these issues. In the data-preprocessing stage, we employ two strategies, data augmentation and transfer learning, to alleviate the problem of an insufficient data volume in deep learning. By using multi-scale convolution, spatial attention mechanisms, and dynamic graph neural networks, our model effectively extracts discriminative features. The MST-DGCN mainly consists of three parts: the multi-scale spatio-temporal module, which extracts multi-scale information and refines spatial attention; the dynamic graph convolution module, which extracts key connectivity information; and the classification module. We conduct experiments on real EEG datasets and achieve an accuracy of 77.89% and a Kappa value of 0.7052, demonstrating the effectiveness of the MST-DGCN in MI-BCI tasks. Our research provides new ideas and methods for the further development of MI-BCI systems. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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44. Motor imagery-based brain–computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients.
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Ma, Zhen-Zhen, Wu, Jia-Jia, Cao, Zhi, Hua, Xu-Yun, Zheng, Mou-Xiong, Xing, Xiang-Xin, Ma, Jie, and Xu, Jian-Guang
- Subjects
- *
ARM , *BRAIN-computer interfaces , *TEMPORAL lobe , *PREFRONTAL cortex , *TREATMENT programs , *FORELIMB - Abstract
Background: The most challenging aspect of rehabilitation is the repurposing of residual functional plasticity in stroke patients. To achieve this, numerous plasticity-based clinical rehabilitation programs have been developed. This study aimed to investigate the effects of motor imagery (MI)-based brain–computer interface (BCI) rehabilitation programs on upper extremity hand function in patients with chronic hemiplegia. Design: A 2010 Consolidated Standards for Test Reports (CONSORT)-compliant randomized controlled trial. Methods: Forty-six eligible stroke patients with upper limb motor dysfunction participated in the study, six of whom dropped out. The patients were randomly divided into a BCI group and a control group. The BCI group received BCI therapy and conventional rehabilitation therapy, while the control group received conventional rehabilitation only. The Fugl–Meyer Assessment of the Upper Extremity (FMA-UE) score was used as the primary outcome to evaluate upper extremity motor function. Additionally, functional magnetic resonance imaging (fMRI) scans were performed on all patients before and after treatment, in both the resting and task states. We measured the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), z conversion of ALFF (zALFF), and z conversion of ReHo (ReHo) in the resting state. The task state was divided into four tasks: left-hand grasping, right-hand grasping, imagining left-hand grasping, and imagining right-hand grasping. Finally, meaningful differences were assessed using correlation analysis of the clinical assessments and functional measures. Results: A total of 40 patients completed the study, 20 in the BCI group and 20 in the control group. Task-related blood-oxygen-level-dependent (BOLD) analysis showed that when performing the motor grasping task with the affected hand, the BCI group exhibited significant activation in the ipsilateral middle cingulate gyrus, precuneus, inferior parietal gyrus, postcentral gyrus, middle frontal gyrus, superior temporal gyrus, and contralateral middle cingulate gyrus. When imagining a grasping task with the affected hand, the BCI group exhibited greater activation in the ipsilateral superior frontal gyrus (medial) and middle frontal gyrus after treatment. However, the activation of the contralateral superior frontal gyrus decreased in the BCI group relative to the control group. Resting-state fMRI revealed increased zALFF in multiple cerebral regions, including the contralateral precentral gyrus and calcarine and the ipsilateral middle occipital gyrus and cuneus, and decreased zALFF in the ipsilateral superior temporal gyrus in the BCI group relative to the control group. Increased zReHo in the ipsilateral cuneus and contralateral calcarine and decreased zReHo in the contralateral middle temporal gyrus, temporal pole, and superior temporal gyrus were observed post-intervention. According to the subsequent correlation analysis, the increase in the FMA-UE score showed a positive correlation with the mean zALFF of the contralateral precentral gyrus (r = 0.425, P < 0.05), the mean zReHo of the right cuneus (r = 0.399, P < 0.05). Conclusion: In conclusion, BCI therapy is effective and safe for arm rehabilitation after severe poststroke hemiparesis. The correlation of the zALFF of the contralateral precentral gyrus and the zReHo of the ipsilateral cuneus with motor improvements suggested that these values can be used as prognostic measures for BCI-based stroke rehabilitation. We found that motor function was related to visual and spatial processing, suggesting potential avenues for refining treatment strategies for stroke patients. Trial registration: The trial is registered in the Chinese Clinical Trial Registry (number ChiCTR2000034848, registered July 21, 2020). [ABSTRACT FROM AUTHOR]
- Published
- 2024
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45. Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks.
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Khabti, Joharah, AlAhmadi, Saad, and Soudani, Adel
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CONVOLUTIONAL neural networks , *MOTOR imagery (Cognition) , *ELECTROENCEPHALOGRAPHY , *SPATIAL filters , *BRAIN-computer interfaces , *CLASSIFICATION , *GENETIC algorithms - Abstract
The widely adopted paradigm in brain–computer interfaces (BCIs) involves motor imagery (MI), enabling improved communication between humans and machines. EEG signals derived from MI present several challenges due to their inherent characteristics, which lead to a complex process of classifying and finding the potential tasks of a specific participant. Another issue is that BCI systems can result in noisy data and redundant channels, which in turn can lead to increased equipment and computational costs. To address these problems, the optimal channel selection of a multiclass MI classification based on a Fusion convolutional neural network with Attention blocks (FCNNA) is proposed. In this study, we developed a CNN model consisting of layers of convolutional blocks with multiple spatial and temporal filters. These filters are designed specifically to capture the distribution and relationships of signal features across different electrode locations, as well as to analyze the evolution of these features over time. Following these layers, a Convolutional Block Attention Module (CBAM) is used to, further, enhance EEG signal feature extraction. In the process of channel selection, the genetic algorithm is used to select the optimal set of channels using a new technique to deliver fixed as well as variable channels for all participants. The proposed methodology is validated showing 6.41% improvement in multiclass classification compared to most baseline models. Notably, we achieved the highest results of 93.09% for binary classes involving left-hand and right-hand movements. In addition, the cross-subject strategy for multiclass classification yielded an impressive accuracy of 68.87%. Following channel selection, multiclass classification accuracy was enhanced, reaching 84.53%. Overall, our experiments illustrated the efficiency of the proposed EEG MI model in both channel selection and classification, showing superior results with either a full channel set or a reduced number of channels. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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46. Recruiting neural field theory for data augmentation in a motor imagery brain-computer interface.
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Polyakov, Daniel, Robinson, Peter A., Muller, Eli J., Shriki, Oren, Farong Gao, and Patow, Gustavo A.
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DATA augmentation ,MOTOR imagery (Cognition) ,BRAIN-computer interfaces ,FRACTAL dimensions ,THALAMOCORTICAL system ,TIME series analysis ,MEAN field theory - Abstract
We introduce a novel approach to training data augmentation in brain-computer interfaces (BCIs) using neural field theory (NFT) applied to EEG data from motor imagery tasks. BCIs often suffer from limited accuracy due to a limited amount of training data. To address this, we leveraged a corticothalamic NFT model to generate artificial EEG time series as supplemental training data. We employed the BCI competition IV '2a' dataset to evaluate this augmentation technique. For each individual, we fitted the model to common spatial patterns of each motor imagery class, jittered the fitted parameters, and generated time series for data augmentation. Our method led to significant accuracy improvements of over 2% in classifying the "total power" feature, but not in the case of the "Higuchi fractal dimension" feature. This suggests that the fit NFT model may more favorably represent one feature than the other. These findings pave the way for further exploration of NFT-based data augmentation, highlighting the benefits of biophysically accurate artificial data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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47. Editorial: Recent advancements in brain-computer interfaces-based limb rehabilitation
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Kishor Lakshminarayanan, Deepa Madathil, Bhaskar Mohan Murari, and Rakshit Shah
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brain-computer interface (BCI) ,wheelchair ,music therapy ,virtual reality ,robot ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Published
- 2024
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48. A Study to Explore the Altered State of Consciousness Using Brain–Computer Interface (BCI)
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Sharma, Pradeep Kumar, Dadheech, Pankaj, Gupta, Mukesh Kumar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Rathore, Vijay Singh, editor, Piuri, Vincenzo, editor, Babo, Rosalina, editor, and Tiwari, Vivek, editor
- Published
- 2024
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49. A Relevant Prototype Domain Gradient Projection Continual Learning Method for Cross-Subject P300 Brain-Computer Interfaces
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Wu, Zhicong, Cai, Honghua, Ling, Yuyan, Pan, Jiahui, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Zhang, Chuanlei, editor, and Chen, Wei, editor
- Published
- 2024
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50. A Deep Dive into Brain-Computer Interface
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Alzubi, Jafar A., Subhra, Snahil, Mishra, Sushruta, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Fortino, Giancarlo, editor, Kumar, Akshi, editor, Swaroop, Abhishek, editor, and Shukla, Pancham, editor
- Published
- 2024
- Full Text
- View/download PDF
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