Search

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]

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by energy diffusion and partial disconnection in the brain, with its main feature being an insidious onset and subtle clinical symptoms. Electroencephalogram (EEG) as a primary tool for assessing and aiding in the diagnosis of brain diseases has been widely used in AD detection. Accurate diagnosis is crucial for preventing the transition from early cognitive impairment to AD and providing early treatment for AD patients. This study aims to establish a hybrid model based on the Improved Artificial Fish Swarm Algorithm (IAFS) and Genetic Algorithm (GA)—IAFS–GA, to determine the optimal channel combination for AD detection under multiple EEG signals. Geometric features and complexity features of AD EEG signals were extracted using Second Order Difference Plot (SODP) and entropy analysis across the full frequency band. Subsequently, Pearson correlation was used for feature ranking, selecting the six least correlated features for each channel. The Relief algorithm was then used to fuse these selected features, with one fused feature representing one channel. Based on this, a feature selection optimization algorithm, IAFS–GA, combining the improved artificial fish swarm algorithm and genetic algorithm, was proposed. Finally, the feature combination was input into a Naive Bayes classifier for the identification of AD patients and normal controls. The feature combination was input into a Naive Bayes classifier for the identification of AD patients and normal controls. Using a five-fold cross-validation strategy across the entire frequency band, the classification accuracy reached 93.53%, with a sensitivity of 98.74%, specificity of 98.25%, and an AUC area of 97.82%. This framework can quickly select appropriate brain channels to enhance the efficiency of detecting AD and other neurological diseases. Moreover, it is the first time that an improved artificial fish swarm genetic combination algorithm and SODP features has been used for channel selection in EEG, proving to be an effective method for AD detection. It is based on SODP analysis, entropy analysis, and intelligent algorithms, which can assist clinicians in rapidly diagnosing AD, reducing the misdiagnosis rate of false positives, and expanding our understanding of brain function in patients with neurological diseases. [ABSTRACT FROM AUTHOR]

To improve the accuracy of quantitative precipitation products derived from regional satellite observations, the level 1 radiation observation products, level 2 cloud detection products, and level 2 quantitative precipitation estimation operational products from the FY-4B geostationary radiation imager AGRI (Advanced Geosynchronous Radiation Imager), and national rain gauge observations collected between June 1 and July 31, 2022, in China were utilized in this study. The random forest algorithm was employed to establish separate models for predicting the presence or absence of rainfall and estimating rainfall amounts during the day and at night. The importance of 15 channels from the AGRI level 1 radiation observations was evaluated, and suitable channels for determining and estimating ground rainfall during the day and night were selected. Judgment and retrieval models based on these optimal channels were then created. Finally, using the models based on their respective optimal channels, precipitation for August 2022 was estimated and compared with the FY-4B AGRI level 2 quantitative precipitation estimation operational products. The results are as follows: (1) Channels 7 and 8 in the mid-wave infrared range were of lower importance during the day, and the daytime judgment and retrieval models did not use these channels. However, the nighttime models employed all infrared channels, and the retrieval precipitation closely matched the national gauge observations. (2) The optimal channel judgment models for both day and night outperformed the FY-4B precipitation operational products in identifying whether precipitation occurred, particularly showing a significant improvement in hit rates. Nevertheless, rainfall retrieval from both optimal channel retrieval models tended to overestimate rainfall amounts, with daytime retrieval precipitation being more accurate than nighttime. (3) The errors of rainfall retrieval from both daytime and nighttime optimal channel models were both lower than those from the FY-4B precipitation operational products. Both the precipitation operational products and the two optimal channel model estimates tended to overestimate light rainfall and underestimate heavy rainfall, with the underestimation becoming more severe as precipitation intensity increased. (4) For the retrieval of different precipitation magnitudes using optimal channel models, improvements in root mean square errors were observed for nearly all levels of rainfall except for nighttime heavy rain, with the most significant accuracy enhancement seen in the small to moderate rainfall range.

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 We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU’s ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels.

AbstractThe brain-computer interface (BCI) systems based on motor imagery typically rely on a large number of electrode channels to acquire information. The rational selection of electroencephalography (EEG) channel combinations is crucial for optimizing computational efficiency and enhancing practical applicability. However, evaluating all potential channel combinations individually is impractical. This study aims to explore a strategy for quickly achieving a balance between maximizing channel reduction and minimizing precision loss. To this end, we developed a spatio-temporal attention perception network named STAPNet. Based on the channel contributions adaptively generated by its subnetwork, we propose an extended step bi-directional search strategy that includes variable ratio channel selection (VRCS) and strided greedy channel selection (SGCS), designed to enhance global search capabilities and accelerate the optimization process. Experimental results show that on the High Gamma and BCI Competition IV 2a public datasets, the framework respectively achieved average maximum accuracies of 91.47% and 84.17%. Under conditions of zero precision loss, the average number of channels was reduced by a maximum of 87.5%. Additionally, to investigate the impact of neural information loss due to channel reduction on the interpretation of complex brain functions, we employed a heatmap visualization algorithm to verify the universal importance and complete symmetry of the selected optimal channel combination across multiple datasets. This is consistent with the brain’s cooperative mechanism when processing tasks involving both the left and right hands. [ABSTRACT FROM AUTHOR]

We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU's ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels. [ABSTRACT FROM AUTHOR]

Stroke is a major cause of mortality worldwide, disrupts cerebral blood flow, leading to severe brain damage. Hemiplegia, a common consequence, results in motor task loss on one side of the body. Many stroke survivors face long-term motor impairments and require great rehabilitation. Electroencephalograms (EEGs) provide a non-invasive method to monitor brain activity and have been used in brain–computer interfaces (BCIs) to help in rehabilitation. Motor imagery (MI) tasks, detected through EEG, are pivotal for developing BCIs that assist patients in regaining motor purpose. However, interpreting EEG signals for MI tasks remains challenging due to their complexity and low signal-to-noise ratio. The main aim of this study is to focus on optimizing channel selection in EEG-based BCIs specifically for stroke rehabilitation. Determining the most informative EEG channels is crucial for capturing the neural signals related to motor impairments in stroke patients. In this paper, a binary bat algorithm (BA)-based optimization method is proposed to select the most relevant channels tailored to the unique neurophysiological changes in stroke patients. This approach is able to enhance the BCI performance by improving classification accuracy and reducing data dimensionality. We use time–entropy–frequency (TEF) attributes, processed through automated independent component analysis with wavelet transform (AICA-WT) denoising, to enhance signal clarity. The selected channels and features are proved through a k-nearest neighbor (KNN) classifier using public BCI datasets, demonstrating improved classification of MI tasks and the potential for better rehabilitation outcomes. [ABSTRACT FROM AUTHOR]

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition that affects millions of children. In this manuscript, we propose methods to classify two groups of children, viz. healthy children, and children with ADHD, providing supplementary information to the doctors for analysis and prediction. An electroencephalogram (EEG) correlation and EEG channel selection are used to discriminate between ADHD EEG and healthy children's EEG. The primary objective of the channel selection method is to reduce the dimensions of the data, reduce computational complexity, improve model performance, and provide faster processing. We use EEG signals recorded in three different scenarios. We propose two models for channel selection using Pearson's correlation coefficient and Hoeffding's D correlation coefficient. Various nonlinear features are extracted from selected channels and used with multiple classifiers. The performance of different classifiers has been tested by calculating the accuracy, precision, recall, and ROC curves among different datasets. The proposed algorithms achieved similar or better accuracy above 90 %with significantly fewer features and channels. [ABSTRACT FROM AUTHOR]

Although deep learning technique has achieved significant improvement on image compression, but its advantages are not fully explored in video compression, which leads to the performance of deep-learning-based video compression (DLVC) is obviously inferior to that of hybrid video coding framework. In this article, we proposed a novel network to improve the performance of DLVC from its most important modules, including Motion Process (MP), Residual Compression (RC), and Frame Reconstruction (FR). In MP, we design a split second-order attention and multi-scale feature extraction module to fully remove the warping artifacts from multi-scale feature space and pixel space, which can help reduce the distortion in the following process. In RC, we propose a channel selection mechanism to gradually drop redundant information while preserving informative channels for a better rate-distortion performance. Finally, in FR, we introduce a residual multi-scale recurrent network to improve the quality of the current reconstructed frame by progressively exploiting temporal context information between it and its several previous reconstructed frames. Extensive experiments are conducted on the three widely used video compression datasets (HEVC, UVG, and MCL-JVC), and the performance demonstrates the superiority of our proposed approach over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Objective: Motivated by Health Care 4.0, this study aims to reducing the dimensionality of traditional EEG features based on manual extracted features, including statistical features in the time and frequency domains. Methods: A total of 22 multi-scale features were extracted from the UNM and Iowa datasets using a 4th order Butterworth filter and wavelet packet transform. Based on single-channel validation, 29 channels with the highest R2 scores were selected from a pool of 59 common channels. The proposed channel selection scheme was validated on the UNM dataset and tested on the Iowa dataset to compare its generalizability against models trained without channel selection. Results: The experimental results demonstrate that the proposed model achieves an optimal classification accuracy of 100%. Additionally, the generalization capability of the channel selection method is validated through out-of-sample testing based on the Iowa dataset Conclusions: Using single-channel validation, we proposed a channel selection scheme based on traditional statistical features, resulting in a selection of 29 channels. This scheme significantly reduced the dimensionality of EEG feature vectors related to Parkinson's disease by 50%. Remarkably, this approach demonstrated considerable classification performance on both the UNM and Iowa datasets. For the closed-eye state, the highest classification accuracy achieved was 100%, while for the open-eye state, the highest accuracy reached 93.75%. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG)-based P300 speller aids in restoring the communication and control capabilities in patients suffering from motor disabilities. However, the quality and quantity of the data collected from EEG recordings have a substantial influence on the P300 speller's performance. Hence, selecting the optimum number of recording electrodes, i.e., channels for each user, is a significant difficulty for the P300 speller. There are two fundamental objectives of the channel selection process: (1) to extract the most crucial information from the relevant channels, hence reducing the computing complexity of P300/non-P300 signal processing operation, and (2) to lessen the potential overfitting that could result from using unwanted channels to boost performance. For obtaining the best channel subsets, different channel selection techniques, including manual, filtering, wrapper, and embedded approaches, have been applied by past researchers. This research provides an in-depth examination of recent advancements, status, challenges, and potential solutions related to channel selection strategies in P300 speller systems. Each channel selection technique is thoroughly explored, including detailed comparisons between them. The notable advantages and drawbacks of each method are emphasized along with the discussion on the future direction and scope of work in the field of channel selection in P300 speller. The review underscores that channel selection methods enable the use of a reduced number of channels without compromising classification performance. By eliminating noisy or irrelevant channels, these approaches contribute to enhanced system performance. [ABSTRACT FROM AUTHOR]

Human-computer interface (HCI) and electroencephalogram (EEG) signals are widely used in user experience (UX) interface designs to provide immersive interactions with the user. In the context of UX, EEG signals can be used within a metaverse system to assess user engagement, attention, emotional responses, or mental workload. By analyzing EEG signals, system designers can tailor the virtual environment, content, or interactions in real time to optimize UX, improve immersion, and personalize interactions. However, in this case, in addition to the signals' processing cost and classification accuracy, cybersickness in Virtual Reality (VR) systems needs to be resolved. At this point, channel selection methods can perform better for HCI and UX applications by reducing noisy and redundant information in generally unrelated EEG channels. For this purpose, a new method for EEG channel selection based on phase-locking value (PLV) analysis is proposed. We hypothesized that there are interactions between EEG channels in terms of PLV in repeated tasks in different trials of the emotion estimation experiment. Subsequently, frequency-based features were extracted. The features were classified by dividing them into bags using the Multiple-Instance Learning (MIL) variant. This study provides higher classification performance using fewer EEG channels for emotion prediction. The performance rate obtained in binary classification with the Random Forests (RF) algorithm is at a promising level of 99%. The proposed method achieved an accuracy of 99.38% for valence using all channels on the new dataset (VREMO) and 98.13% with channel selection. The benchmark dataset (DEAP) achieved accuracies of 98.16% using all channels and 98.13% with selected channels.

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]

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]

Weakly Supervised Object Localization (WSOL) aims to utilize the features learned by a classifier on the image-level labels to locate target objects. However, these existing channel selection methods for WSOL still cannot effectively select the important channels and remove the unimportant ones. To address this issue, we propose a Clustering-inspired Channel Selection method based on Class Activation Maps (CCS-CAM). Compared with the traditional methods, the advantage of CCS-CAM is that it is very simple yet effective for channel selection due to the K-means clustering based on Class Activation Maps. It can effectively ensure both object localization and classification accuracy. The effectiveness of the proposed CCS-CAM method has been demonstrated using multiple public datasets, with GT-Know Loc reaching 87.9% and 63.71% on the CUB200-2011 and ImageNet-1k respectively, which is superior to the other state-of-the-art methods. • WSOL network extends image classification for precise object location. • CCS module filters channels, using K-means for effective object localization. • Tested on CUB200-2011, ImageNet-1K, FGVC-Aircraft, Stanford Cars with success. [ABSTRACT FROM AUTHOR]

In practical Electromyography (EMG) control, it is necessary to select appropriate number of surface EMG channels with ideal classification performance. In this paper, the contribution of different muscles to motor tasks is taken as the optimization criterion for optimal channel selection, and a channel selection method based on muscle synergy(MS) is proposed. Firstly, the raw EMG signal is preprocessed to extract EMG features, and then non-negative matrix factorization (NMF) algorithm is used to extract the muscle synergy matrix for each gesture and converted. Secondly, the muscle weight coefficients of each gesture on each EMG channel are summed to obtain the importance coefficients of all EMG channels. Finally, the classification is carried out by support vector machine(SVM), random forest(RF) and K-nearest neighbor(KNN) classifier. The method is tested by using surface EMG recorded in DB5 subdatabase of Ninapro database. The test results show that when extracting 10 optimal channels, compared with the sequential forward selection(SFS), Markov random field(MRF) and Relief-F channel selection methods proposed in previous studies, the recognition accuracy of the EMG subsets determined by this method is similar to that obtained by MRF and Relief-F method, and is slightly lower than that obtained by SFS method, but the computational cost is lower than that of SFS, MRF and Relief-F methods. [ABSTRACT FROM AUTHOR]

The number of electrode channels in a brain-computer interface (BCI) affects not only its classification performance, but also its convenience in practical applications. Despite many studies on channel selection in motor imagery (MI)-based BCI systems, they consist in matrix analysis of EEG signals, which inevitably loses the interactive information among multiple domains such as space, time and frequency. In this paper, a tensor decomposition-based channel selection (TCS) method is employed for MI BCIs. A three-way tensor is yielded by wavelet transform of a single-trial EEG signal and decomposed into three factor matrices by a regularized canonical polyadic decomposition (CPD). The channel factor matrix is used for channel selection and the important channels are selected by calculating the correlation between channels. Regularized common spatial pattern (RCSP) is employed for feature extraction and support vector machine (SVM) for classification. The proposed TCS-RCSP algorithm was evaluated on three BCI data sets and compared with the RCSP with all channels (AC-RCSP) and the RCSP with selected channels by correlation-based channel selection method (CCS-RCSP). The results indicate that TCS-RCSP achieved significantly better overall accuracy than AC-RCSP (94.4% vs. 86.3%) with ρ < 0.01 and CCS-RCSP (94.4% vs. 90.2%) with ρ < 0.05, proving the efficacy of the proposed algorithm for classifying MI tasks. [ABSTRACT FROM AUTHOR]

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]

Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain–computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain–computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III–IV(A) and BCI competition IV–I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques. [ABSTRACT FROM AUTHOR]

This article delves into the investigation of parameters and associated issues in the context of a Bayesian game theory-based approach for selecting the target channel for secondary users in a licensed heterogeneous cognitive radio network. In such networks, cognitive or secondary users can enhance their activity within a licensed spectrum by dynamically selecting an available spectrum, thereby addressing the scarcity issue in the radio spectrum. The study adopts a game model where each user is treated as a network player, with the channel payoff or utility function serving as a crucial parameter for target channel selection. The game theory approach assumes that each player possesses knowledge of other players' payoffs, although this assumption may only hold for some games. In auction-based games, for instance, other players' payoffs or profits are unknown, introducing uncertainty. This type of game model falls under the Bayesian game model. The article presents the Bayesian battle of the sexes approach to address uncertainty in other players' payoffs or profits and proposes possible causes for channel selection. The best response for secondary users and channel payoffs is computed using this approach. The Bayesian Nash equilibrium calculates the average payoff, taking into account various spectrum handoff parameters like waiting time and inactive probability. This comprehensive analysis aims to calculate the overall network characteristics in the given heterogeneous cognitive radio environment. [ABSTRACT FROM AUTHOR]

This study utilized an infrared spotlight Hyperspectral infrared Atmospheric Sounder (HIRAS) and the Medium Resolution Spectral Imager (MERSI) mounted on FY3D cloud products from the National Satellite Meteorological Center of China to obtain methane profile information. Methane inversion channels near 7.7 μm were selected based on the different distribution of methane weighting functions across different seasons and latitudes, and the selected retrieval channels had a great sensitivity to methane but not to other parameters. The optimization method was employed to retrieve methane profiles using these channels. The ozone profiles, temperature, and water vapor of the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis data (ERA5) were applied to the retrieval process. After validating the methane profile concentrations retrieved by HIRAS, the following conclusions were drawn: (1) compared with Civil Aircraft for the Regular Investigation of the Atmosphere Based on an Instrument Container (CARIBIC) flight data, the average correlation coefficient, relative difference, and root mean square error were 0.73, 0.0491, and 18.9 ppbv, respectively, with lower relative differences and root mean square errors in low-latitude regions than in mid-latitude regions. (2) The methane profiles retrieved from May 2019 to September 2021 showed an average error within 60 ppbv compared with the Fourier transform infrared spectrometer (FTIR) station observations of the Infrared Working Group (IRWG) of the Network for the Detection of Atmospheric Composition Change (NDACC). The errors between the a priori and retrieved values, as well as between the retrieved and smoothed values, were larger by around 400–500 hPa. Apart from Toronto and Alzomoni, which had larger peak values in autumn and spring respectively, the mean column averaging kernels typically has a larger peak in summer. [ABSTRACT FROM AUTHOR]

Microwave hyperspectral instruments represent one of the main atmospheric sounders of China's next-generation Fengyun meteorological satellites. In order to better apply microwave hyperspectral observations in the fields of atmospheric parameter retrieval and data assimilation, this paper analyzes the sensitivity of trace gases to five selected bandwidth channels using a radiative transfer model based on the simulated data of microwave hyperspectral radiances at 50–60 GHz. This method uses information entropy and a weighting function to select channels and analyze the impact of this on the retrieval accuracy of atmospheric profiles before and after channel selection. The experimental results show that channel selection can reduce the number of channels by approximately 74.05% while maintaining a large amount of information content, and this retrieval effect is significantly better than that of MWTS-III. After channel selection, the 10 MHz, 30 MHz, and 50 MHz bandwidths have the best retrieval results in the stratosphere, whole atmosphere, and troposphere, respectively. When considering the number of channels, computational scale, and retrieval results comprehensively, the channel selection method is effective. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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.)

Gesture recognition has emerged as a significant research domain in computer vision and human-computer interaction. One of the key challenges in gesture recognition is how to select the most useful channels that can effectively represent gesture movements. In this study, we have developed a channel selection algorithm that determines the number and placement of sensors that are critical to gesture classification. To validate this algorithm, we constructed a Force Myography (FMG)-based signal acquisition system. The algorithm considers each sensor as a distinct channel, with the most effective channel combinations and recognition accuracy determined through assessing the correlation between each channel and the target gesture, as well as the redundant correlation between different channels. The database was created by collecting experimental data from 10 healthy individuals who wore 16 sensors to perform 13 unique hand gestures. The results indicate that the average number of channels across the 10 participants was 3, corresponding to an 75% decrease in the initial channel count, with an average recognition accuracy of 94.46%. This outperforms four widely adopted feature selection algorithms, including Relief-F, mRMR, CFS, and ILFS. Moreover, we have established a universal model for the position of gesture measurement points and verified it with an additional five participants, resulting in an average recognition accuracy of 96.3%. This study provides a sound basis for identifying the optimal and minimum number and location of channels on the forearm and designing specialized arm rings with unique shapes.

Brain-computer interfaces (BCIs) can enable direct communication with assistive devices by recording and decoding signals from the brain. To achieve high performance, many electrodes will be used, such as the recently developed invasive BCIs with channel numbers up to hundreds or even thousands. For those high-throughput BCIs, channel selection is important to reduce signal redundancy and invasiveness while maintaining decoding performance. However, such endeavour is rarely reported for invasive BCIs, especially those using deep learning methods. Two deep learning-based methods, referred to as Gumbel and STG, were proposed in this paper. They were evaluated using the Stereo-electroencephalography (SEEG) signals, and compared with three other methods, including manual selection, mutual information-based method (MI), and all channels (all channels without selection). The task is to classify the SEEG signals into five movements using channels selected by each method. When 10 channels were selected, the mean classification accuracies using Gumbel, STG (referred to as STG-10), manual selection, and MI selection were 65%, 60%, 60%, and 47%, respectively, whilst the accuracy was 59% using all channels (no selection). In addition, an investigation of the selected channels showed that Gumbel and STG have successfully identified the pre-central and post-central areas, which are closely related to motor control. Both Gumbel and STG successfully selected the informative channels in SEEG recordings while maintaining decoding accuracy. This study enables future high-throughput BCIs using deep learning methods, to identify useful channels and reduce computing and wireless transmission pressure.

Gaze estimation, as a technique that reflects individual attention, can be used for disability assistance and assisting physicians in diagnosing diseases such as autism spectrum disorder (ASD), Parkinson’s disease, and attention deficit hyperactivity disorder (ADHD). Various techniques have been proposed for gaze estimation and achieved high resolution. Among these approaches, electrooculography (EOG)-based gaze estimation, as an economical and effective method, offers a promising solution for practical applications. Objective: In this paper, we systematically investigated the possible EOG electrode locations which are spatially distributed around the orbital cavity. Afterward, quantities of informative features to characterize physiological information of eye movement from the temporal-spectral domain are extracted from the seven differential channels. Methods and procedures: To select the optimum channels and relevant features, and eliminate irrelevant information, a heuristical search algorithm (i.e., forward stepwise strategy) is applied. Subsequently, a comparative analysis of the impacts of electrode placement and feature contributions on gaze estimation is evaluated via 6 classic models with 18 subjects. Results: Experimental results showed that the promising performance was achieved both in the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) within a wide gaze that ranges from −50° to +50°. The MAE and RMSE can be improved to 2.80° and 3.74° ultimately, while only using 10 features extracted from 2 channels. Compared with the prevailing EOG-based techniques, the performance improvement of MAE and RMSE range from 0.70° to 5.48° and 0.66° to 5.42°, respectively. Conclusion: We proposed a robust EOG-based gaze estimation approach by systematically investigating the optimal channel/feature combination. The experimental results indicated not only the superiority of the proposed approach but also its potential for clinical application. Clinical and translational impact statement: Accurate gaze estimation is a key step for assisting disabilities and accurate diagnosis of various diseases including ASD, Parkinson’s disease, and ADHD. The proposed approach can accurately estimate the points of gaze via EOG signals, and thus has the potential for various related medical applications.

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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.)

In recent times, EEG-based emotion recognition has gained significant attention in affective computing. One of the major challenges in designing an efficient EEG-based emotion-recognition framework lies in handling the high dimensionality of the data recorded from a large number of EEG electrodes. This paper develops a hybrid sequential forward channel selection (HSFCSER) based emotion-recognition method, which aims to identify the most relevant subset of EEG channels and thus reduces the dimensionality of data. In HSFCSER, the Fisher score is used as the filter method, and the wrapper method includes Support Vector Machine (SVM). Wavelet-based features are extracted from the selected optimal EEG channels, followed by the feature selection using a multi-objective genetic algorithm. The proposed method is evaluated on the DEAP database. The methodology identifies 1) two classes of emotions viz. Low/High Valence with an average accuracy of 91.25 ± 5.48% for subject-dependent and 89.38 ± 4.76% for subject-independent, and Low/High Arousal with an average accuracy of 89.92 ± 5.81% for subject-dependent and 86.32 ± 5.34% for subject-independent, and 2) four classes of emotions viz. High Valence-Low Arousal (HVLA), High Valence-High Arousal, Low Valence-Low Arousal, and Low Valence-High Arousal with 75.94 ± 11.07% for subject-dependent and 73.13 ± 8.17% for subject-independent. The efficient electrode positions for emotion recognition are also depicted. The reported results are better compared to the existing results in the literature. The source code of the proposed work is made available at https://github.com/shyammarjit/HSFCS. [ABSTRACT FROM AUTHOR]

In the domain of neuroscience, electroencephalography (EEG) holds a pivotal role in determining the inner workings of the human brain, offering real-time insights into cognitive processes, emotions, and neurological disorders. While numerous EEG channels are available in a typical EEG brain-computer interface, selecting the optimal subset for emotion classification poses a significant challenge. Conventional channel selection methods overlook the biological relevance of specific brain lobes in emotional processing, leading to a lack of contextual specificity. This paper introduces a novel approach, by using a biologically informed channel selection approach in the EEG signals. The brain is segmented into various groups and sub-groups and the ability of the channels associated with those groups is determined using time and frequency domain features. The ability of each of these groups and sub-groups to attain higher performance is determined through the accuracy outcomes driven by the support vector machines (SVM). The ability of the selected channels in making accurate classification has been determined using a deep learning model in determining valence and arousal classes, and making a comparison with the selected channels-led classification methods. The approach is validated using the DEAP dataset, demonstrating its potential to enhance EEG-based emotion classification accuracy and efficiency. This innovative methodology offers a promising avenue for future EEG research, allowing customization based on the specific emotions under study, psychological intervention, and streamlining the setup process while maintaining the highest levels of accuracy, reaching an average of 95.7% for intra-subject and 94.65% for cross-subject emotion classification. [ABSTRACT FROM AUTHOR]

Stroke is a major cause of mortality worldwide, disrupts cerebral blood flow, leading to severe brain damage. Hemiplegia, a common consequence, results in motor task loss on one side of the body. Many stroke survivors face long-term motor impairments and require great rehabilitation. Electroencephalograms (EEGs) provide a non-invasive method to monitor brain activity and have been used in brain–computer interfaces (BCIs) to help in rehabilitation. Motor imagery (MI) tasks, detected through EEG, are pivotal for developing BCIs that assist patients in regaining motor purpose. However, interpreting EEG signals for MI tasks remains challenging due to their complexity and low signal-to-noise ratio. The main aim of this study is to focus on optimizing channel selection in EEG-based BCIs specifically for stroke rehabilitation. Determining the most informative EEG channels is crucial for capturing the neural signals related to motor impairments in stroke patients. In this paper, a binary bat algorithm (BA)-based optimization method is proposed to select the most relevant channels tailored to the unique neurophysiological changes in stroke patients. This approach is able to enhance the BCI performance by improving classification accuracy and reducing data dimensionality. We use time–entropy–frequency (TEF) attributes, processed through automated independent component analysis with wavelet transform (AICA-WT) denoising, to enhance signal clarity. The selected channels and features are proved through a k-nearest neighbor (KNN) classifier using public BCI datasets, demonstrating improved classification of MI tasks and the potential for better rehabilitation outcomes.

Objective: Motivated by Health Care 4.0, this study aims to reducing the dimensionality of traditional EEG features based on manual extracted features, including statistical features in the time and frequency domains. Methods: A total of 22 multi-scale features were extracted from the UNM and Iowa datasets using a 4th order Butterworth filter and wavelet packet transform. Based on single-channel validation, 29 channels with the highest R2 scores were selected from a pool of 59 common channels. The proposed channel selection scheme was validated on the UNM dataset and tested on the Iowa dataset to compare its generalizability against models trained without channel selection. Results: The experimental results demonstrate that the proposed model achieves an optimal classification accuracy of 100%. Additionally, the generalization capability of the channel selection method is validated through out-of-sample testing based on the Iowa dataset Conclusions: Using single-channel validation, we proposed a channel selection scheme based on traditional statistical features, resulting in a selection of 29 channels. This scheme significantly reduced the dimensionality of EEG feature vectors related to Parkinson’s disease by 50%. Remarkably, this approach demonstrated considerable classification performance on both the UNM and Iowa datasets. For the closed-eye state, the highest classification accuracy achieved was 100%, while for the open-eye state, the highest accuracy reached 93.75%.

Abstract In multichannel signal processing with distributed sensors, choosing the optimal subset of observed sensor signals to be exploited is crucial in order to maximize algorithmic performance and reduce computational load, ideally both at the same time. In the acoustic domain, signal cross-correlation is a natural choice to quantify the usefulness of microphone signals, i.e., microphone utility, for coherent array processing, but its estimation requires that the uncoded signals are synchronized and transmitted between nodes. In resource-constrained environments like acoustic sensor networks, low data transmission rates often make transmission of all observed signals to the centralized location infeasible, thus discouraging direct estimation of signal cross-correlation. Instead, we employ characteristic features of the recorded signals to estimate the usefulness of individual microphone signals using the Magnitude-Squared Coherence (MSC) between the source and respective microphone signal as ground-truth metric. In this contribution, we provide a comprehensive analysis of model-based microphone utility estimation approaches that use signal features and, as an alternative, also propose machine learning-based estimation methods that identify optimal sensor signal utility features. The performance of both approaches is validated experimentally using both simulated and recorded acoustic data, comprising a variety of realistic and practically relevant acoustic scenarios including moving and static sources.

Purpose: In this research, a recyclable waste is used to manufacture a specific product. For this reason, a supply chain is considered containing manufacturer, recycler, and waste warehouse. First, the customers' demand for the considered product is determined based on its price. Then, the manufacturer produces it from a waste with specific recyclability rate. The waste warehouse collects the waste and the recycler recycles it. The manufacturer meets his requirements through two different channels. He can procure the non-recycled waste from the waste warehouse and then recycles it himself, or can buy the recycled waste from the recycler. The manufacturer selects these channels based on the established situations. Methodology: The game theory is applied to make the decisions under two considered channels. In this setting, a Stackelberg game is developed based on the competitive situation established among the members, where the manufacturer has higher decision power than the waste warehouse and recycler. Findings: Eventually, the given strategies are discussed and the obtained results are presented. Results indicate that the manufacturer selects each channel as a threshold is met. Moreover, more recyclability rate of the considered waste leads to higher profits for the members. Originality/Value: In this research, to provide the waste materials required for producing a product, the game-theoretic approach as well as the concept of the channel-selection are used. It can be stated that this issue has been proposed for the first time in the literature. [ABSTRACT FROM AUTHOR]

In this study, the effectiveness of six machine learning and eight deep learning algorithms in analyzing electroencephalogram (EEG) signals for detecting epileptic seizures has been investigated. The study utilizes 14 channels in the EMOTIV EPOC+ device which is based on international 10-20 system. To find out the most informative and sensitive channel, one of the 14 channels has been dropped one at a time. The accuracy values were determined for all the methods using two different publicly available datasets: the Guinea-Bissau epilepsy dataset and the Nigeria epilepsy dataset. In case of machine learning models, the performance of SVM classifier performs best with maximum accuracy of 83.2% (Guinea-Bissau) and 77% (Nigeria) without excluding any channels. No significant performance degradation has been observed for single channel exclusion of this classifier. Among the deep learning models, the four best performing models in terms of accuracy are CNN-LSTM (92.5%), IC-RNN (91.8%), ChronoNet (91.1%) and C-DRNN (88.6%). After excluding one channel at a time and investigating their effect on the performance of the four DL models, it has been observed that the most significant and most sensitive channels lie within the frontal and parietal zone. This finding will be very useful in practice as it indicates that the electrodes in the frontal and parietal zone should be placed with absolute precision for accurate diagnosis of the diseases. In addition, this study also explore the effectiveness of the selected classifiers in detecting seizure in case of failure of any particular EEG signal channel. [ABSTRACT FROM AUTHOR]

Cognitive Radio is playing a crucial role to enhance radio spectrum utilization by applying various techniques for real-time and non-real-time services. In this work, we have proposed an aggregation and fragmentation of bandwidth-based channel allocation model which uses the Cognitive Radio concept to allocate the channels effectively. In the model, services are categorized into four heterogeneous classes. Of this, Primary new and Primary handoff services are of real-time in nature while Secondary new and Secondary handoff services are of non-real-time in nature. The network is also categorized into two: fixed network and dynamic network categories to enhance the spectrum utilization and to minimize the call block and call drop. Performance analysis, along with the comparative results, exhibit the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Introduction:During electroencephalography (EEG)-basedmotor imagery-brain-computer interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume much computational resources. Therefore, channel selection is crucial while ensuring classification accuracy. Methods: This paper proposes a channel selection method by integrating the efficient channel attention (ECA) module with a convolutional neural network (CNN). During model training process, the ECA module automatically assigns the channel weights by evaluating the relative importance for BCI classification accuracy of every channel. Then a ranking of EEG channel importance can be established so as to select an appropriate number of channels to form a channel subset from the ranking. In this paper, the ECA module is embedded into a commonly used network for MI, and comparative experiments are conducted on the BCI Competition IV dataset 2a. Results and discussion: The proposed method achieved an average accuracy of 75.76% with all 22 channels and 69.52% with eight channels in a four-class classification task, outperforming other state-of-the-art EEG channel selection methods. The result demonstrates that the proposedmethod provides an effective channel selection approach for EEG-based MI-BCI. [ABSTRACT FROM AUTHOR]

Many current brain–computer interface (BCI) applications depend on the quick processing of brain signals. Most researchers strive to create new methods for future implementation and enhance existing models to discover an optimal feature set that can operate independently. This study focuses on four key concepts that will be used to complete future works. The first concept is related to potential future communication models, whereas the others aim to enhance previous models or methodologies. The four concepts are as follows. First, we suggest a new communication imagery model as a substitute for a speech imager that relies on a mental task approach. As speech imagery is intricate, one cannot imagine the sounds of every character in every language. Our study proposes a new mental task model for lip-sync imagery that can be employed in all languages. Any character in any language can be used with this mental task model. In this study, we utilized two lip-sync movements to indicate two sounds, characters, or letters. Second, we considered innovative hybrid signals. Choosing an unsuitable frequency range can lead to ineffective feature extractions. Therefore, the selection of an appropriate frequency range is crucial for processing. The ultimate goal of this method is to accurately discover distinct frequencies of brain imagery activities. The restricted frequency range combination presents an initial proposal for generating fragmented, continuous frequencies. The first model assesses two 4 Hz intervals as filter banks. The primary objective is to discover new combinations of signals at 8 Hz by selecting filter banks with a 4 Hz scale from the frequency range of 4 Hz to 40 Hz. This approach facilitates the acquisition of efficient and clearly defined features by reducing similar patterns and enhancing distinctive patterns of brain activity. Third, we introduce a new linear bond graph classifier as a supplement to a linear support vector machine (SVM) when handling noisy data. The performance of the linear support vector machine (SVM) significantly declines under high-noise conditions. To complement the linear support vector machine (SVM) in noisy-data situations, we introduce a new linear bond graph classifier. Fourth, this paper presents a deep-learning model for formula recognition that converts the first-layer data into a formula extraction model. The primary goal is to decrease the noise in the formula coefficients of the subsequent layers. The output of the final layer comprises coefficients chosen by different functions at various levels. The classifier then extracts the root interval for each formula, and a diagnosis is established based on these intervals. The final goal of the last idea is to explain the main brain imagery activity formula using a combination formula for similar and distinctive brain imagery activities. The results of implementing all of the proposed methods are reported. The results range between 55% and 98%. The lowest result is 55% for the deep detection formula, and the highest result is 98% for new combinations of signals. [ABSTRACT FROM AUTHOR]

Multi-channel electroencephalography (EEG) is used to capture features associated with motor imagery (MI) based brain-computer interface (BCI) with a wide spatial coverage across the scalp. However, redundant EEG channels are not conducive to improving BCI performance. Therefore, removing irrelevant channels can help improve the classification performance of BCI systems. We present a new method for identifying relevant EEG channels. Our method is based on the assumption that useful channels share related information and that this can be measured by inter-channel connectivity. Specifically, we treat all candidate EEG channels as a graph and define channel selection as the problem of node classification on a graph. Then we design a graph convolutional neural network (GCN) model for channels classification. Channels are selected based on the outputs of our GCN model. We evaluate our proposed GCN-based channel selection (GCN-CS) method on three MI datasets. On three datasets, GCN-CS achieves performance improvements by reducing the number of channels. Specifically, we achieve classification accuracies of 79.76% on Dataset 1, 89.14% on Dataset 2 and 87.96% on Dataset 3, which outperform competing methods significantly. [ABSTRACT FROM AUTHOR]