Search

Carbon-based nanomaterials, such as carbon quantum dots (CQDs) and carbon 2D nanosheets (graphene, graphene oxide, and graphdiyne), have shown remarkable potential in various biological applications. CQDs offer tunable photoluminescence and excellent biocompatibility, making them suitable for bioimaging, drug delivery, biosensing, and photodynamic therapy. Additionally, CQDs' unique properties enable bioimaging-guided therapy and targeted imaging of biomolecules. On the other hand, carbon 2D nanosheets exhibit exceptional physicochemical attributes, with graphene excelling in biosensing and bioimaging, also in drug delivery and antimicrobial applications, and graphdiyne in tissue engineering. Their properties, such as tunable porosity and high surface area, contribute to controlled drug release and enhanced tissue regeneration. However, challenges, including long-term biocompatibility and large-scale synthesis, necessitate further research. Potential future directions encompass theranostics, immunomodulation, neural interfaces, bioelectronic medicine, and expanding bioimaging capabilities. In summary, both CQDs and carbon 2D nanosheets hold promise to revolutionize biomedical sciences, offering innovative solutions and improved therapies in diverse biological contexts. Addressing current challenges will unlock their full potential and can shape the future of medicine and biotechnology. [ABSTRACT FROM AUTHOR]

Currently, brain–computer interfaces (BCIs) have become an important part of advanced human-computer interactions. Its essence is to infer people's ideas or purposes through brain signals to realize human-computer communication. BCI systems based on steady-state visual-evoked potentials (SSVEP) are widely used in the field of BCI because of their high information transfer rate (ITR), low training requirements, and simple system structure. To explore a reasonable design method of visual stimulation in an SSVEP-BCI interactive interface, this study completed two experiments based on the shape and presentation of visual stimulation. Experiment 1 explored the optimal shapes of different visual stimuli. The results indicated that there were significant differences in task completion times for different stimulus shapes. Combined with ergonomics experiment and subjective evaluation results, the circular was the optimal stimulus shape. Experiment 2 used different numbers of auxiliary stimuli under the condition of circular stimulus. The results showed significant differences in task completion time for various auxiliary stimuli, and the number of auxiliary stimuli negatively correlated with task completion time. Through subjective evaluation, it was found that all the median scores of the usability index increased when there were more auxiliary stimuli. The recommended number of auxiliary stimuli was eight. This research provides significant evidence for the design of visual stimulation for SSVEP-BCI systems. The experimental conclusions are of great value for improving the interaction efficiency of SSVEP-BCI systems, enhancing user experience, and expanding application fields. They will also provide new directions and ideas for the interface design of BCI systems, visual stimulation design, and application of advanced interaction technology. [ABSTRACT FROM AUTHOR]

This article examines the effects of technology in the field of education and management and focuses especially on the effects of technologies used in distance education activities on transformation processes. Based on research conducted, the article explains how technological developments affect education and management processes, according to the findings obtained as a result of the interviews. The technologies used in the digitalization processes of higher education institutions that provide distance education services and the effects of these technologies on the transformation processes were examined. How artificial intelligence, blockchain, metaverse, brain-computer interfaces and similar technologies can be used in education and organizational management processes and how this use affects management processes are discussed. Findings show that technological developments have profound effects on the processes in educational environments and transform management processes. As a result, the research emphasizes the need for further research and application to effectively use technologies such as artificial intelligence, blockchain, and metaverse in education and management processes. It is stated that advances in this field can cause significant transformations in education and management. [ABSTRACT FROM AUTHOR]

Introduction: Decoding an individual's hidden brain states in responses to musical stimuli under various cognitive loads can unleash the potential of developing a non-invasive closed-loop brain-machine interface (CLBMI). To perform a pilot study and investigate the brain response in the context of CLBMI, we collect multimodal physiological signals and behavioral data within the workingmemory experiment in the presence of personalizedmusical stimuli. Methods: Participants perform a working memory experiment called the n-back task in the presence of calming music and exciting music. Utilizing the skin conductance signal and behavioral data, we decode the brain's cognitive arousal and performance states, respectively. We determine the association of oxygenated hemoglobin (HbO) data with performance state. Furthermore, we evaluate the total hemoglobin (HbT) signal energy over each music session. Results: A relatively low arousal variation was observed with respect to task difficulty, while the arousal baseline changes considerably with respect to the type of music. Overall, the performance index is enhanced within the exciting session. The highest positive correlation between the HbO concentration and performance was observed within the higher cognitive loads (3-back task) for all of the participants. Also, the HbT signal energy peak occurs within the exciting session. Discussion: Findings may underline the potential of using music as an intervention to regulate the brain cognitive states. Additionally, the experiment provides a diverse array of data encompassingmultiple physiological signals that can be used in the brain state decoder paradigm to shed light on the human-in-the-loop experiments and understand the network-level mechanisms of auditory stimulation. [ABSTRACT FROM AUTHOR]

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]

A recent development in deep learning techniques has attracted attention to the decoding and classification of electroencephalogram (EEG) signals. Despite several efforts to utilize different features in EEG signals, a significant research challenge is using time-dependent features in combination with local and global features. Several attempts have been made to remodel the deep learning convolution neural networks (CNNs) to capture time-dependency information. These features are usually either handcrafted features, such as power ratios, or splitting data into smaller-sized windows related to specific properties, such as a peak at 300 ms. However, these approaches partially solve the problem but simultaneously hinder CNNs' capability to learn from unknown information that might be present in the data. Other approaches, like recurrent neural networks, are very suitable for learning time-dependent information from EEG signals in the presence of unrelated sequential data. To solve this, we have proposed an encoding kernel (EnK), a novel time-encoding approach, which uniquely introduces time decomposition information during the vertical convolution operation in CNNs. The encoded information lets CNNs learn time-dependent features in addition to local and global features. We performed extensive experiments on several EEG data sets--physical human-robot collaborations, P300 visual-evoked potentials, motor imagery, movement-related cortical potentials, and the Dataset for Emotion Analysis Using Physiological Signals. The EnK outperforms the state of the art with an up to 6.5% reduction in mean squared error (MSE) and a 9.5% improvement in F1-scores compared to the average for all data sets together compared to base models. These results support our approach and show a high potential to improve the performance of physiological and non-physiological data. Moreover, the EnK can be applied to virtually any deep learning architecture with minimal effort. [ABSTRACT FROM AUTHOR]

In this study, we integrated virtual reality (VR) goggles and a motor imagery (MI) brain-computer interface (BCI) algorithm with a lower-limb rehabilitation exoskeleton robot (LLRER) system. The MI-BCI system was integrated with the VR goggles to identify the intention classification system. The VR goggles enhanced the immersive experience of the subjects during data collection. The VR-enhanced electroencephalography (EEG) classification model of a seated subject was directly applied to the rehabilitation of the LLRER wearer. The experimental results showed that the VR goggles had a positive effect on the classification accuracy of MI-BCI. The best results were obtained with subjects in a seated position wearing VR, but the seated VR classification model cannot be directly applied to rehabilitation triggers in the LLRER. There were a number of confounding factors that needed to be overcome. This study proposes a cumulative distribution function (CDF) auto-leveling method that can apply the seated VR model to standing subjects wearing exoskeletons. The classification model of seated VR had an accuracy of 75.35% in the open-loop test of the LLRER, and the accuracy of correctly triggering the rehabilitation action in the closed-loop gait rehabilitation of LLRER was 74%. Preliminary findings regarding the development of a closed-loop gait rehabilitation system activated by MI-BCI were presented. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology 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.)

Brain-computer interface (BCI) has been widely used to capture electrical signals generated from the brain. One of the most commonly used methods in the BCI system is the electroencephalogram (EEG). However, processing brain signals is challenging and requires a lot of computation processes. This study selected single-channel EEG as the input command for a portable soft exoskeleton glove system. This research aims to develop an affordable soft exoskeleton glove driven by single-channel EEG for people with impaired hand motion. We proposed an intuitive control method that feels more natural by imagining hand movements. Eighteen healthy participants underwent EEG data collection while their brain activity (attention level) was measured under four controlled conditions: listening to preferred music with lyrics, listening to disliked music, pre-workout state, and post-workout state. These variations in attention level, mood, and physical exertion influenced the measured EEG signals to drive the soft glove. Ttest was applied to determine the significant difference for noise environment and physical variation tests. Those EEG signals are used to drive the linear actuator and provide mechanical assistance. Simple on/off control was embedded in the soft glove microcontroller to control the finger flexion/extension based on the EEG signal as a command. The result shows that the proposed wearable soft exoskeleton glove driven by EEG signal can be a potential assistive device for people with hand impairment. The speed for the soft glove was 3 seconds to close completely from a fully open. For optimal performance, this system needs to be used in a calm and distraction-free environment when the user is well-rested. [ABSTRACT FROM AUTHOR]

In this study, the effects of electrode porosity on nerve regeneration and functional recovery after sciatic nerve transection in rats was investigated. A sieve-type neural electrode with 70% porosity was designed and compared with an electrode with 30% porosity. Electrodes were fabricated from photosensitive polyimide and implanted into the transected sciatic nerves. Motor function recovery was evaluated using the Sciatic Function Index. The number of active channels and their signal quality were recorded and analyzed to assess the sensory neural signal acquisition. Electrical impedance spectroscopy was used to evaluate the electrode performance. The group implanted with the 70% porosity electrode demonstrated significantly enhanced nerve regeneration and motor function recovery, approaching control group levels by the fifth week. In contrast, the group with the 30% porosity electrode exhibited limited improvement. Immunohistochemical analysis confirmed extensive nerve fiber growth within the 70% porous structure. Moreover, the 70% porosity electrode consistently acquired neural signals from more channels compared to the 30% porosity electrode, demonstrating its superior performance in sensory signal detection. These findings emphasize the importance of optimizing electrode porosity in the development of advanced neural interfaces, with the potential to enhance clinical outcomes in peripheral nerve repair and neuroprosthetic applications. [ABSTRACT FROM AUTHOR]

Background: A key challenge in steady-state visual evoked potential (SSVEP)- based brain-computer interface (BCI) systems is to effectively recognize frequencies within a short time window. To address this challenge, the specific characteristics of the data are needed to select the frequency recognition method. These characteristics include factors, such as the number of stimulation targets and the presence of harmonic frequencies, resulting in optimizing the performance and accuracy of SSVEP-based BCI systems. Objective: The current study aimed to examine the effect of data characteristics on frequency recognition accuracy. Material and Methods: In this analytical study, five commonly used frequency recognition methods were examined, used to various datasets containing different numbers of frequencies, including sub-data with and without frequency harmonics. Results: The increase in the number of frequencies in the Multivariate Linear Regression (MLR) method has led to a decrease in frequency recognition accuracy by 9%. Additionally, the presence of harmonic frequencies resulted in an 8% decrease in accuracy for the MLR method. Conclusion: Frequency recognition using the MLR method reduces the effect of the number of different frequencies and harmonics of the stimulation frequencies on the frequency recognition accuracy. [ABSTRACT FROM AUTHOR]

Significance: Recently developed miniaturized neural recording devices that can monitor and perturb neural activity in freely behaving animals have significantly expanded our knowledge of neural underpinning of complex behaviors. Most miniaturized neural interfaces require a wired connection for external power and data acquisition systems. The wires are required to be commutated through a slip ring to accommodate for twisting of the wire or tether and alleviate torsional stresses. The increased trend toward long-term continuous neural recordings has spurred efforts to realize active commutators that can sense the torsional stress and actively rotate the slip ring to alleviate torsional stresses. Current solutions however require the addition of sensing modules. Aim: Here, we report on an active translating commutator that uses computer vision (CV) algorithms on behavioral imaging videos captured during the experiment to track the animal's position and heading direction in real time and uses this information to control the translation and rotation of a slip ring commutator to accommodate for accumulated mouse heading orientation changes and position. Approach: The CV-guided active commutator has been extensively tested in three separate behavioral contexts. Results: We show reliable cortex-wide imaging in a mouse in an open field with a miniaturized wide-field cortical imaging device. Active commutation resulted in no changes to measured neurophysiological signals. Conclusion: The active commutator is fully open source, can be assembled using readily available off-the-shelf components, and is compatible with a wide variety of miniaturized neurophotonic and neurophysiology devices. [ABSTRACT FROM AUTHOR]

Each application of neurostimulators requires unique stimulation parameter specifications to achieve effective stimulation. Balancing the current magnitude with stimulation resolution, waveform, size, and channel count is challenging, leading to a loss of generalizability across broad neural interfaces. To address this, this paper proposes a highly scalable, programmable neurostimulator with a System-on-Chip (SOC) capable of 32 channels of independent stimulation. The compliance voltage reaches up to ±22.5 V. A pair of 8-bit current-mode DACs support independent waveforms for source and sink operations and feature a user-selectable dual range for low-current intraparenchymal microstimulation with a resolution of 4.31 μ A/bit, as well as high current stimulation for spinal cord and DBS applications with a resolution of 48.00 μ A/bit, achieving a wide stimulation range of 12.24 mA while maintaining high-resolution biological stimulation. A dedicated communication protocol enables full programmable control of stimulation waveforms, effectively improving the range of stimulation parameters. In vivo electrophysiological experiments successfully validate the functionality of the proposed stimulator. This flexible stimulator architecture aims to enhance its generality across a wide range of neural interfaces and will provide more diverse and refined stimulation strategies. [ABSTRACT FROM AUTHOR]

This paper explores cognitive interface technology, aiming to tackle current challenges and shed light on the prospects of brain-computer interfaces (BCIs). It provides a comprehensive examination of their transformative impact on medical technology and patient well-being. Specifically, this study contributes to addressing challenges in classifying brain lesion images arising from the complex nature of lesions and limitations of traditional deep learning approaches. It introduces advanced feature fusion models that leverage deep learning algorithms, including the African vulture optimization (AVO) algorithm. These models integrate informative features from multiple pre-trained networks and employ innovative fusion techniques, including the attention-driven grid feature fusion (ADGFF) model. The ADGFF model incorporates an attention mechanism based on the optimized weights obtained using AVO. The objective is to improve the overall accuracy by providing fine-grained control over different regions of interest in the input image through a grid-based technique. This grid-based technique divides the image into vertical and horizontal grids, simplifying the exemplar feature generation process without compromising performance. Experimental results demonstrate that the proposed feature fusion strategies consistently outperform individual pre-trained models in terms of accuracy, sensitivity, specificity, and F1-score. The optimized feature fusion strategies, particularly the GRU-ADGFF model, further enhance classification performance, outperforming CNN and RNN classifiers. The learning progress analysis shows convergence, indicating the effectiveness of the feature fusion strategies in capturing lesion patterns. AUC-ROC curves highlight the superior discriminatory capabilities of the ADGFF-AVO strategy. Five-fold cross-validation is employed to assess the performance of the proposed models, demonstrating their accuracy, and few other accuracy-based measures. The GRU-ADGFF model optimized with AVO consistently achieves high accuracy, sensitivity, and AUC values, demonstrating its effectiveness and generalization capability. The GRU-ADGFF model also outperforms the majority voting ensemble technique in terms of accuracy and discriminative ability. Additionally, execution time analysis reveals good scalability and resource utilization of the proposed models. The Friedman rank test confirms significant differences in classifier performance, with the GRU-ADGFF model emerging as the top-performing method across different feature fusion strategies and optimization algorithms. [ABSTRACT FROM AUTHOR]

Background: The current study explores the integration of a motor imagery (MI)-based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients. Methods: We developed a tablet deployable BCI control of the virtual iTbot for ease of use. Twelve right-handed healthy adults participated in this study, which involved a novel BCI training approach incorporating tactile vibration stimulation during MI tasks. The experiment utilized EEG signals captured via a gel-free cap, processed through various stages including signal verification, training, and testing. The training involved MI tasks with concurrent vibrotactile stimulation, utilizing common spatial pattern (CSP) training and linear discriminant analysis (LDA) for signal classification. The testing stage introduced a real-time feedback system and a virtual game environment where participants controlled a virtual iTbot robot. Results: Results showed varying accuracies in motor intention detection across participants, with an average true positive rate of 63.33% in classifying MI signals. Discussion: The study highlights the potential of MI-based BCI in robotic rehabilitation, particularly in terms of engagement and personalization. The findings underscore the feasibility of BCI technology in rehabilitation and its potential use for stroke survivors with upper limb dysfunctions. [ABSTRACT FROM AUTHOR]

The past two decades have witnessed dramatic advancement in computer‐aided design (CAD). However, development of human–computer interfaces (HCI) for CAD have not kept up with these advances. Windows, Icons, Menus, Pointer (WIMP) is still the mainly used interface for CAD applications which limits the naturalness and intuitiveness of the CAD modeling process. As a novel interface, Brain–computer interfaces (BCIs) have great potential in the application of CAD modeling. Utilizing BCIs, the user can create CAD models just by thinking about it in principle, because BCIs provide an end‐to‐end interaction channel between users and CAD models. However, current related studies are mainly limited to the existing BCIs paradigms, while ignoring the relationship between electroencephalogram (EEG) signals and CAD models, which largely increases the cognitive load on the users. In this study, we aimed to explore the potential of using BCI to create CAD models directly independent of the classical BCIs paradigms. For this purpose, EEG signals evoked by six basic CAD models (i.e., point, square, trapezoid, line, triangle, and circle) were collected from 28 participants. After preprocessing and sub‐trial principal components analysis (st‐PCA) of recorded data, the peak, mean and time‐frequency energy features were extracted from EEG signals. By applying the one‐way repeated measures analysis of variance, we demonstrated that there were significant differences among these EEG features evoked by different CAD models. These features from EEG electrode channels ranked by mutual information were then used to train a discriminant classifier of genetic algorithm‐based support vector machine. The empirical result showed that this classifier can discriminate the CAD models with an average accuracy of about 72%, which turns out that EEG based model generation is feasible, and provides the technical and theoretical basis for building a novel BCI for CAD modeling. [ABSTRACT FROM AUTHOR]

Over the past decade, a wide range of brain–computer interface (BCI) applications with artificial intelligence (AI) assistance have emerged. While using a head-mounted electroencephalogram (EEG) device for BCI application, it often requires a user to wear the device close to the scalp so that clear EEG signals can be obtained from the head directly. Those signals can be used to determine a user's mental state. Due to the head size differences between Chinese and European users, as well as the differences between Chinese users themselves, EEG headsets with fixed-electrode-point will enable some electrodes fail to capture a user's EEG data accurately. Combined with previous research and related design methodology, the design of a head-mounted EEG device is proposed in this paper. In this study, Chinese youth are regarded as the target user group. After evaluating different designs of device structures, the possible positions and movement methods of electrodes, an EEG headset with movable electrodes for young Chinese users is finally delivered. [ABSTRACT FROM AUTHOR]

Tactile Imagery (TI) remains a fairly understudied phenomenon despite growing attention to this topic in recent years. Here, we investigated the effects of TI on corticospinal excitability by measuring motor evoked potentials (MEPs) induced by single-pulse transcranial magnetic stimulation (TMS). The effects of TI were compared with those of tactile stimulation (TS) and kinesthetic motor imagery (kMI). Twenty-two participants performed three tasks in randomly assigned order: imagine finger tapping (kMI); experience vibratory sensations in the middle finger (TS); and mentally reproduce the sensation of vibration (TI). MEPs increased during both kMI and TI, with a stronger increase for kMI. No statistically significant change in MEP was observed during TS. The demonstrated differential effects of kMI, TI and TS on corticospinal excitability have practical implications for devising the imagery-based and TS-based brain–computer interfaces (BCIs), particularly the ones intended to improve neurorehabilitation by evoking plasticity changes in sensorimotor circuitry. [ABSTRACT FROM AUTHOR]

Microglia are important players in surveillance and repair of the brain. Implanting an electrode into the cortex activates microglia, produces an inflammatory cascade, triggers the foreign body response, and opens the blood-brain barrier. These changes can impede intracortical brain-computer interfaces performance. Using two-photon imaging of implanted microelectrodes, we test the hypothesis that low-intensity pulsed ultrasound stimulation can reduce microglia-mediated neuroinflammation following the implantation of microelectrodes. In the first week of treatment, we found that low-intensity pulsed ultrasound stimulation increased microglia migration speed by 128%, enhanced microglia expansion area by 109%, and a reduction in microglial activation by 17%, indicating improved tissue healing and surveillance. Microglial coverage of the microelectrode was reduced by 50% and astrocytic scarring by 36% resulting in an increase in recording performance at chronic time. The data indicate that low-intensity pulsed ultrasound stimulation helps reduce the foreign body response around chronic intracortical microelectrodes. Implanting an electrode into the cortex activates microglia, triggers the foreign body response, and impedes intracortical brain-computer interface. Here, the authors apply low-intensity pulsed ultrasound stimulation (LIPUS) after probe insertion to reduce microglia-mediated neuroinflammation. [ABSTRACT FROM AUTHOR]

In recent years, with the shift of focus in metaverse research toward content exchange and social interaction, breaking through the current bottleneck of audiovisual media interaction has become an urgent issue. The use of brain-machine interfaces for sensory simulation is one of the proposed solutions. Currently, brainmachine interfaces have demonstrated irreplaceable potential as physiological signal acquisition tools in various fields within the metaverse. This study explores three application scenarios: generative art in the metaverse, serious gaming for healthcare in metaverse medicine, and brain-machine interface applications for facial expression synthesis in the virtual society of the metaverse. It investigates existing commercial products and patents (such as MindWave Mobile, GVS, and Galea), draws analogies with the development processes of network security and neurosecurity, bioethics and neuroethics, and discusses the challenges and potential issues that may arise when brain-machine interfaces mature and are widely applied. Furthermore, it looks ahead to the diverse possibilities of deep and varied applications of brain-machine interfaces in the metaverse in the future. [ABSTRACT FROM AUTHOR]

Background: Error-related potentials (ErrPs) are electrophysiological responses that naturally occur when humans perceive wrongdoing or encounter unexpected events. It offers a distinctive means of comprehending the error-processing mechanisms within the brain. A method for detecting ErrPs with high accuracy holds significant importance for various ErrPs-based applications, such as human-in-the-loop Brain-Computer Interface (BCI) systems. Nevertheless, current methods fail to fulfill the generalization requirements for detecting such ErrPs due to the high non-stationarity of EEG signals across different tasks and the limited availability of ErrPs datasets. Methods: This study introduces a deep learning-based model that integrates convolutional layers and transformer encoders for the classification of ErrPs. Subsequently, a model training strategy, grounded in transfer learning, is proposed for the effective training of themodel. The datasets utilized in this study are available for download from the publicly accessible databases. Results: In cross-task classification, an average accuracy of about 78% was achieved, exceeding the baseline. Furthermore, in the leave-one-subject-out, within-session, and cross-session classification scenarios, the proposed model outperformed the existing techniques with an average accuracy of 71.81, 78.74, and 77.01%, respectively. Conclusions: Our approach contributes to mitigating the challenge posed by limited datasets in the ErrPs field, achieving this by reducing the requirement for extensive training data for specific target tasks. This may serve as inspiration for future studies that concentrate on ErrPs and their applications. [ABSTRACT FROM AUTHOR]

This research presents a dataset consisting of electroencephalogram and eye tracking recordings obtained from six patients with amyotrophic lateral sclerosis (ALS) in a locked-in state and one hundred seventy healthy individuals. The ALS patients exhibited varying degrees of disease progression, ranging from partial mobility and weakened speech to complete paralysis and loss of speech. Despite these physical impairments, the ALS patients retained good eye function, which allowed them to use a virtual keyboard for communication. Data from ALS patients was recorded multiple times at their homes, while data from healthy individuals was recorded once in a laboratory setting. For each data recording, the experimental design involved nine recording sessions per participant, each corresponding to a common human action or demand. This dataset can serve as a valuable benchmark for several applications, such as improving spelling systems with brain-computer interfaces, investigating motor imagination, exploring motor cortex function, monitoring motor impairment progress in patients undergoing rehabilitation, and studying the effects of ALS on cognitive and motor processes. [ABSTRACT FROM AUTHOR]

Brain–Computer Interface is tested as a successful method in improving human cognitive functions such as attention and memory. Attention plays a significant role in areas ranging from a person's day-to-day life to educational domain and professional activities. When attention is evaluated using camera-based techniques, the users may suffer privacy issues. Using Brain–Computer Interface (BCI) to obtain a measure of attention will be useful in this regard. The paper proposes a Human Attention Recognition System (HARS) in which EEG signal acquisition is used to obtain the attention of the individual, Renyi's entropy-based mutual information method is used for feature selection and a deep learning-based classifier is used to classify the signals. HADS is not camera-based: therefore, faces of the subjects are not revealed. EEG signals were collected using the Ultracortex Mark III dry electrodes and were visualised using OpenBCI GUI (Graphical User Interface). The experiment is validated using the publicly available Confused Student EEG dataset from Kaggle, giving an accuracy of 99.21%. The results indicate that the proposed method can identify attention levels accurately and can be effectively used in educational systems, biofeedback and medical research. [ABSTRACT FROM AUTHOR]

Although brain-computer interface (BCI) is considered a revolutionary advancement in human-computer interaction and has achieved significant progress, a considerable gap remains between the current technological capabilities and their practical applications. To promote the translation of BCI into practical applications, the gold standard for online evaluation for classification algorithms of BCI has been proposed in some studies. However, few studies have proposed a more comprehensive evaluation method for the entire online BCI system, and it has not yet received sufficient attention from the BCI research and development community. Therefore, the qualitative leap from analyzing and modeling for offline BCI data to the construction of online BCI systems and optimizing their performance is elaborated, and then user-centred is emphasized, and then the comprehensive evaluation methods for translating BCI into practical applications are detailed and reviewed in the article, including the evaluation of the usability (including effectiveness and efficiency of systems), the evaluation of the user satisfaction (including BCI-related aspects, etc.), and the evaluation of the usage (including the match between the system and user, etc.) of online BCI systems. Finally, the challenges faced in the evaluation of the usability and user satisfaction of online BCI systems, the efficacy of online BCI systems, and the integration of BCI and artificial intelligence (AI) and/or virtual reality (VR) and other technologies to enhance the intelligence and user experience of the system are discussed. It is expected that the evaluation methods for online BCI systems elaborated in this review will promote the translation of BCI into practical applications. [ABSTRACT FROM AUTHOR]

Movement-related cortical potential (MRCP) is observed in EEG recordings prior to a voluntary movement. It has been used for e.g., quantifying motor learning and for brain-computer interfacing (BCIs). The MRCP amplitude is affected by various factors, but the effect of caffeine is underexplored. The aim of this study was to investigate if a cup of coffee with 85 mg caffeine modulated the MRCP amplitude and the classification of MRCPs versus idle activity, which estimates BCI performance. Twenty-six healthy participants performed 2 × 100 ankle dorsiflexion separated by a 10-min break before a cup of coffee was consumed, followed by another 100 movements. EEG was recorded during the movements and divided into epochs, which were averaged to extract three average MRCPs that were compared. Also, idle activity epochs were extracted. Features were extracted from the epochs and classified using random forest analysis. The MRCP amplitude did not change after consuming caffeine. There was a slight increase of two percentage points in the classification accuracy after consuming caffeine. In conclusion, a cup of coffee with 85 mg caffeine does not affect the MRCP amplitude, and improves MRCP-based BCI performance slightly. The findings suggest that drinking coffee is only a minor confounder in MRCP-related studies. [ABSTRACT FROM AUTHOR]

This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain–computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, using multiclass classification based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and minimum-norm estimate algorithms were implemented to select discriminant channels and enhance the EEG data. Hence, deep EEGNet and convolutional recurrent neural networks were separately implemented to classify the EEG data for image visualization into 40 labels. Using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures. The satisfactory results obtained with this method offer a new implementation opportunity for multitask embedded BCI applications utilizing a reduced number of both channels (<50%) and network parameters (<110 K). [ABSTRACT FROM AUTHOR]

A motor imagery brain–computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis. [ABSTRACT FROM AUTHOR]

Benchmarking brain–computer interface (BCI) applications, considering all available smart training environment (STE) criteria, is a challenging task due to the following issues: inconsistent weights, static ranks and ranking stability measurements. Therefore, this study aims to develop a dynamic decision-making framework for benchmarking BCI applications based on STE criteria through three integrated phases. In the first phase, the adaptivity of the decision matrix is identified concerning two dimensions: 27 BCI applications as alternatives and 10 STE criteria. In the second phase, the consistency of weights is evaluated and constructed to each STE criterion via the fuzzy-weighted zero-inconsistency (FWZIC) method and the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method for benchmarking the BCI applications. In the third phase, four sensitive scenarios are developed for measuring the consistency of the STE criteria's weights and the ranking performance of the BCI applications. The experimental result shows that the 'ease of use' STE criterion obtains a high-affected weight with a value of 0.13, while other criteria, augmented reality, hybrid and desktop use (stationary), obtain less weight with a 0.075 value. Additionally, BCI applications A5 and A6 are robust and stable among the others based on the consistency of weights concerning the four scenarios, and they are further candidates to be deployed in real-life applications. The overall ranking results are stable and less affected when applied to the four sensitive scenarios due to the robustness of the integrated FWZIC-VIKOR method of the proposed dynamic framework. The outcome of this framework is objectively validated in terms of five groups, and the ranking results are reliable and the closest to the decision-makers' viewpoints. The proposed framework considers a good solution for choosing a dependable application to support the user and community of BCI systems with a stable STE environment. [ABSTRACT FROM AUTHOR]

Electrocorticogram (ECoG) is an electrophysiological signal that results from the summation of neuronal activity near the cortical surface. To record ECoG signals, the scalp and skull are surgically opened and electrodes are placed on the cortical surface, either epidurally or subdurally. Owing to its improved spatiotemporal resolution and signal quality compared with electroencephalography, it is widely used to diagnose and treat neurological disorders in clinical settings for several decades, despite the invasiveness of ECoG. Recently, ECoG is applied in research to explore brain functions and connectivity, brain‐computer interfaces, and brain‐machine interfaces. In addition to the need for ECoG in neuroscience research, ECoG devices have advanced in terms of materials, fabrication, and function to overcome the limitations of commercially available ECoG arrays. Here, the conventional use of ECoG in clinical medicine, the new applications of ECoG in basic neuroscience research, and the future challenges in translating recent developments in ECoG devices for clinical use are described. [ABSTRACT FROM AUTHOR]

A brain-computer interface (BCI) enables users to control devices with their minds. Despite advancements, non-invasive BCIs still exhibit high error rates, prompting investigation into the potential reduction through concurrent targeted neuromodulation. Transcranial focused ultrasound (tFUS) is an emerging non-invasive neuromodulation technology with high spatiotemporal precision. This study examines whether tFUS neuromodulation can improve BCI outcomes, and explores the underlying mechanism of action using high-density electroencephalography (EEG) source imaging (ESI). As a result, V5-targeted tFUS significantly reduced the error in a BCI speller task. Source analyses revealed a significantly increase in theta and alpha activities in the tFUS condition at both V5 and downstream in the dorsal visual processing pathway. Correlation analysis indicated that the connection within the dorsal processing pathway was preserved during tFUS stimulation, while the ventral connection was weakened. These findings suggest that V5-targeted tFUS enhances feature-based attention to visual motion. Transcranial focused ultrasound (tFUS) is a non-invasive brain modulation technology with high spatiotemporal specificity. Here, authors found tFUS can significantly enhance a brain-computer interface speller by delivering low-intensity tFUS to visual cortex during the task in naïve human subjects. [ABSTRACT FROM AUTHOR]

Introduction: Brain computer interfaces (BCI), which establish a direct interaction between the brain and the external device bypassing peripheral nerves, is one of the hot research areas. How to effectively convert brain intentions into instructions for controlling external devices in real-time remains a key issue that needs to be addressed in brain computer interfaces. The Riemannian geometry-based methods have achieved competitive results in decoding EEG signals. However, current Riemannian classifiers tend to overlook changes in data distribution, resulting in degenerated classification performance in cross-session and/or cross subject scenarios. Methods: This paper proposes a brain signal decoding method based on Riemannian transfer learning, fully considering the drift of the data distribution. Two Riemannian transfer learning methods based log-Euclidean metric are developed, such that historical data (source domain) can be used to aid the training of the Riemannian decoder for the current task, or data from other subjects can be used to boost the training of the decoder for the target subject. Results: The proposed methods were verified on BCI competition III, IIIa, and IV 2a datasets. Compared with the baseline that without transfer learning, the proposed algorithm demonstrates superior classification performance. In contrast to the Riemann transfer learning method based on the affine invariant Riemannian metric, the proposed method obtained comparable classification performance, but is much more computationally efficient. Discussion: With the help of proposed transfer learningmethod, the Riemannian classifier obtained competitive performance to existingmethods in the literature. More importantly, the transfer learning process is unsupervised and time-efficient, possessing potential for online learning scenarios. [ABSTRACT FROM AUTHOR]

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]

Objective: Understanding the neural correlates of naturalistic behavior is critical for extending and confirming the results obtained from trial-based experiments and designing generalizable brain-computer interfaces that can operate outside laboratory environments. In this study, we aimed to pinpoint consistent spectrospatial features of neural activity in humans that can discriminate between naturalistic behavioral states. Approach: We analyzed data from five participants using electrocorticography (ECoG) with broad spatial coverage. Spontaneous and naturalistic behaviors such as "Talking" and "Watching TV" were labeled from manually annotated videos. Linear discriminant analysis (LDA) was used to classify the two behavioral states. The parameters learned from the LDA were then used to determine whether the neural signatures driving classification performance are consistent across the participants. Main results: Spectro-spatial feature values were consistently discriminative between the two labeled behavioral states across participants. Mainly, θ, α, and low and high γ in the postcentral gyrus, precentral gyrus, and temporal lobe showed significant classification performance and feature consistency across participants. Subject-specific performance exceeded 70%. Combining neural activity from multiple cortical regions generally does not improve decoding performance, suggesting that information regarding the behavioral state is nonadditive as a function of the cortical region. Significance: To the best of our knowledge, this is the first attempt to identify specific spectro-spatial neural correlates that consistently decode naturalistic and active behavioral states. The aim of this work is to serve as an initial starting point for developing brain-computer interfaces that can be generalized in a realistic setting and to further our understanding of the neural correlates of naturalistic behavior in humans. [ABSTRACT FROM AUTHOR]

Background: Channel selection has become the pivotal issue a ecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside e ective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems. Methods: In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on di erent multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA. Results: The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the e ectiveness of TS-MOEA. Conclusion: The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can e ectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency. [ABSTRACT FROM AUTHOR]

The optimal placement of a cochlear implant (CI) electrode inside the scala tympani compartment to create an effective electrode--neural interface is the base for a successful CI treatment. The characteristics of an effective electrode design include (a) electrode matching every possible variation in the inner ear size, shape, and anatomy, (b) electrically covering most of the neuronal elements, and (c) preserving intra-cochlear structures, even in non-hearing preservation surgeries. Flexible electrode arrays of various lengths are required to reach an angular insertion depth of 680° to which neuronal cell bodies are angularly distributed and to minimize the rate of electrode scalar deviation. At the time of writing this article, the current scientific evidence indicates that straight lateral wall electrode outperforms perimodiolar electrode by preventing electrode tip fold-over and scalar deviation. Most of the available literature on electrode insertion depth and hearing outcomes supports the practice of physically placing an electrode to cover both the basal and middle turns of the cochlea. This is only achievable with longer straight lateral wall electrodes as single-sized and preshaped perimodiolar electrodes have limitations in reaching beyond the basal turn of the cochlea and in offering consistent modiolar hugging placement in every cochlea. For malformed inner ear anatomies that lack a central modiolar trunk, the perimodiolar electrode is not an effective electrode choice. Most of the literature has failed to demonstrate superiority in hearing outcomes when comparing perimodiolar electrodes with straight lateral wall electrodes from single CI manufacturers. In summary, flexible and straight lateral wall electrode type is reported to be gentle to intra-cochlear structures and has the potential to electrically stimulate most of the neuronal elements, which are necessary in bringing full benefit of the CI device to recipients. [ABSTRACT FROM AUTHOR]

The classification performance of endogenous electroencephalogram (EEG) brain-computer interfaces (BCIs) can be improved by hybridizing the paradigm through the use of commands from multiple paradigms. Hybrid paradigms using motor imagery (MI) and speech imagery (SI) have shown promise, but there is a lack of research into: i) their effectiveness when compared to pure MI and SI for multiclass problems, and ii) automated command selection. This study investigates multiclass MI and SI hybrid paradigms and compares the results to those obtained using pure MI and SI. Performance was assessed using F1 score and accuracy. The performances of all possible hybrid paradigm designs were assessed. The analysis indicated that hybridization does not always guarantee improved performance when compared to the pure paradigms, and there is inter-subject variation in the best paradigm. This confirmed the need for automated subject-specific hybrid paradigm designs. An automated hybrid paradigm selection technique using successive halving (SH) for expedited computational times was developed and results were compared to those obtained using a standard grid search. The SH approach resulted in an improvement in F1 score of 21.09% and 36.86% compared to MI and SI and led to a reduction in computational times of 82.80% compared to grid search. [ABSTRACT FROM AUTHOR]

Motor imagery (MI) becomes a powerful rehabilitation tool, particularly when combined with brain–computer interfaces (BCI). Therefore, methods to improve MI accuracy are a trending topic in the BCI field. Here, we examined the effects of transcranial direct current stimulation (tDCS) and MI priming on the MI signature in an EEG-based MI-BCI triple-blind study. Thirty healthy younger adults participated in this study and were designated to one of two priming groups: A bimanual tracking task and a MI task as primers for MI-BCI. During the performance of the primer task, participants received anodal and sham tDCS in two randomized sessions with a one-week wash-out period between sessions. Subsequently, participants performed an EEG-driven BCI-MI task. EEG time–frequency analyses revealed that desynchronization of the Beta Region precedes desynchronization in the Alpha Region, implying that the Beta frequency band might be best-suited to extract MI signatures as it could lead to faster MI-BCI. Contrary to our hypotheses, no effect of tDCS or priming task on EEG activity during the BCI-MI task was found. Future research should carefully consider the added value of tDCS and priming tasks BCI performance improvement. Electric field modeling studies and high-definition tDCS motor cortex stimulation might be promising avenues. [ABSTRACT FROM AUTHOR]

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]

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]

Decreased attentional function causes problems in daily life. However, a quick and easy evaluation method of attentional function has not yet been developed. Therefore, we are searching for a method to evaluate attentional function easily and quickly. This study aimed to collect basic data on the features of electroencephalography (EEG) during attention tasks to develop a new method for evaluating attentional function using EEG. Twenty healthy young adults participated; we examined cerebral activity during a Clinical Assessment for Attention using portable EEG devices. The Mann–Whitney U test was performed to assess differences in power levels of EEG during tasks between the low- and high-attention groups. The findings revealed that the high-attention group showed significantly higher EEG power levels in the δ wave of L-temporal and bilateral parietal lobes, as well as in the β and γ waves of the R-occipital lobe, than did the low-attention group during digit-forward, whereas the high-attention group showed significantly higher EEG power levels in the θ wave of R-frontal and the α wave of bilateral frontal lobes during digit-backward. Notably, lower θ, α, and β bands of the right hemisphere found in the low-attention group may be key elements to detect attentional deficit. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) technology is rapidly advancing in medical research and application. As an emerging biomedical engineering technology, it has garnered significant attention in the clinical research of brain disease diagnosis and treatment, neurological rehabilitation, and mental health. However, BCI also raises several challenges and ethical concerns in clinical research. In this article, the authors investigate and discuss three aspects of BCI in medicine and healthcare: the state of international ethical governance, multidimensional ethical challenges pertaining to BCI in clinical research, and suggestive concerns for ethical review. Despite the great potential of frontier BCI research and development in the field of medical care, the ethical challenges induced by itself and the complexities of clinical research and brain function have put forward new special fields for ethics in BCI. To ensure "responsible innovation" in BCI research in healthcare and medicine, the creation of an ethical global governance framework and system, along with special guidelines for cutting-edge BCI research in medicine, is suggested. [ABSTRACT FROM AUTHOR]

This Joel A. DeLisa Lecture on endovascular brain-computer interfaces was presented by Dr Thomas Oxley on February 23, 2023, at the Association of Academic Physiatrists Annual Scientific Meeting. The lecture described how brain-computer interfaces replace lost physiological function to enable direct communication between the brain and external digital devices, such as computers, smartphones, and robotic limbs. Specifically, the potential of a novel endovascular brain-computer interface technology was discussed. The brain-computer interface uses a stent-electrode array delivered via the jugular vein and is permanently implanted in a vein adjacent to the motor cortex. In a first-in-human clinical trial, participants with upper limb paralysiswho received the endovascular brain-computer interface could use the system independently and at home to operate laptop computers for various instrumental activities of daily living. A Food and Drug Administration-approved trial of the endovascular brain-computer interface in the United States is in progress. Future development of the system will provide recipients with continuous autonomy through digital access with minimal caregiver assistance. Physiatrists and occupational therapists will have a vital role in helping people with paralysis achieve the potential of implantable brain-computer interfaces. [ABSTRACT FROM AUTHOR]

In recent years, there has been a considerable amount of research on visual evoked potential (VEP)-based brain–computer interfaces (BCIs). However, it remains a big challenge to detect VEPs elicited by small visual stimuli. To address this challenge, this study employed a 256-electrode high-density electroencephalogram (EEG) cap with 66 electrodes in the parietal and occipital lobes to record EEG signals. An online BCI system based on code-modulated VEP (C-VEP) was designed and implemented with thirty targets modulated by a time-shifted binary pseudo-random sequence. A task-discriminant component analysis (TDCA) algorithm was employed for feature extraction and classification. The offline and online experiments were designed to assess EEG responses and classification performance for comparison across four different stimulus sizes at visual angles of 0.5°, 1°, 2°, and 3°. By optimizing the data length for each subject in the online experiment, information transfer rates (ITRs) of 126.48 ± 14.14 bits/min, 221.73 ± 15.69 bits/min, 258.39 ± 9.28 bits/min, and 266.40 ± 6.52 bits/min were achieved for 0.5°, 1°, 2°, and 3°, respectively. This study further compared the EEG features and classification performance of the 66-electrode layout from the 256-electrode EEG cap, the 32-electrode layout from the 128-electrode EEG cap, and the 21-electrode layout from the 64-electrode EEG cap, elucidating the pivotal importance of a higher electrode density in enhancing the performance of C-VEP BCI systems using small stimuli. [ABSTRACT FROM AUTHOR]

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]

Motor imagery electroencephalogram (MI-EEG) is becoming increasingly important. This paper solves the problem of online signal recognition for motor imagery across subjects by finding common features across multiple subjects to improve the generality of the classification model. We analysed the EEG data from left/right-hand motor imagery of eight subjects and proposed a weighted time-domain (WTD) feature extraction method based on a weighted channel screening method. The classification model constructed by combining this feature extraction method with the support vector machine (SVM) classification method was faster in classification and achieved good cross-subject classification accuracy (The average offline classification accuracy was 91.39%). In this paper, an online control system for asynchronous brain-controlled wheelchairs was built with good performance. The online average motor imagery classification accuracy was 81.67%, and the average response time was 1.36s. This method contributes to bringing the online Brain-computer interface (BCI) system out of the laboratory and into wider application. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) motor imagery (MI) classification refers to the use of EEG signals to identify and classify subjects' motor imagery activities; this task has received increasing attention with the development of brain-computer interfaces (BCIs). However, the collection of EEG data is usually time-consuming and labor-intensive, which makes it difficult to obtain sufficient labeled data from the new subject to train a new model. Moreover, the EEG signals of different individuals exhibit significant differences, leading to a significant drop in the performance of a model trained on the existing subjects when directly classifying EEG signals acquired from new subjects. Therefore, it is crucial to make full use of the EEG data of the existing subjects and the unlabeled EEG data of the new target subject to improve the MI classification performance achieved for the target subject. This research study proposes a semi-supervised multi-source transfer (SSMT) learning model to address the above problems; the model learns informative and domain-invariant representations to address cross-subject MI-EEG classification tasks. In particular, a dynamic transferred weighting schema is presented to obtain the final predictions by integrating the weighted features derived from multi-source domains. The average accuracies achieved on two publicly available EEG datasets reach 83.57 % and 85.09 % , respectively, validating the effectiveness of the SSMT process. The SSMT process reveals the importance of informative and domain-invariant representations in MI classification tasks, as they make full use of the domain-invariant information acquired from each subject. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG), as a tool capable of objectively recording brain electrical signals during emotional expression, has been extensively utilized. Current technology heavily relies on datasets, with its performance being limited by the size of the dataset and the accuracy of its annotations. At the same time, unsupervised learning and contrastive learning methods largely depend on the feature distribution within datasets, thus requiring training tailored to specific datasets for optimal results. However, the collection of EEG signals is influenced by factors such as equipment, settings, individuals, and experimental procedures, resulting in significant variability. Consequently, the effectiveness of models is heavily dependent on dataset collection efforts conducted under stringent objective conditions. To address these challenges, we introduce a novel approach: employing a self‐supervised pre‐training model, to process data across different datasets. This model is capable of operating effectively across multiple datasets. The model conducts self‐supervised pre‐training without the need for direct access to specific emotion category labels, enabling it to pre‐train and extract universally useful features without predefined downstream tasks. To tackle the issue of semantic expression confusion, we employed a masked prediction model that guides the model to generate richer semantic information through learning bidirectional feature combinations in sequence. Addressing challenges such as significant differences in data distribution, we introduced adaptive clustering techniques that manage by generating pseudo‐labels across multiple categories. The model is capable of enhancing the expression of hidden features in intermediate layers during the self‐supervised training process, enabling it to learn common hidden features across different datasets. This study, by constructing a hybrid dataset and conducting extensive experiments, demonstrated two key findings: (1) our model performs best on multiple evaluation metrics; (2) the model can effectively integrate critical features from different datasets, significantly enhancing the accuracy of emotion recognition. [ABSTRACT FROM AUTHOR]

Purpose: First, testing an intervention of neuromodulation based on motor imagery and action observation as a promoter of motor adaptation of a complex motor task involving balance. Second, determining what prior balance factors can affect the motor adaptation task. Methods: A double-blind randomized controlled trial was performed. Forty-eight healthy subjects were recruited. The balance of all participants during gait and standing was assessed before adapting to the complex, multi-limb motor task of riding an inverse steering bicycle (ISB). Two interventions were carried out interleaved among trials of adaptation to the motor task: the experimental group (n = 24) was asked to perform neuromodulation (EN) by watching first-person ISB riding through immersive VR glasses and, simultaneously, mentally mimicking the movements. The control group (CG) was asked to watch a slideshow video of steady landscape images. Results: The results showed that the EN group did not improve the motor adaptation rate and induced higher adaptation times with respect to the CG. However, while the motor adaptation success showed a significant dependence on the prior proprioceptive participation in balance in the CG, the EN group did not present any relationship between the prior balance profile and motor adaptation outcome. Conclusions: Results point to a benefit of the visually guided neuromodulation for the motor adaptation of the subjects with low participation of proprioception in balance. Moreover, the results from the control group would allow to disclose prognostic factors about the success of the motor adaptation, and also prescription criteria for the proposed neuromodulation based on the balance profile. [ABSTRACT FROM AUTHOR]

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]