Classification Of Motor Imagery Tasks Research Articles

EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning

Brain-computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite their potential, achieving accurate and robust classification of MI tasks using electroencephalography (EEG) data remains a significant challenge. In this paper, we employed the Minimum Redundancy Maximum Relevance (MRMR) algorithm to optimize channel selection. Furthermore, we introduced a hybrid optimization approach that combines the War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). This hybridization significantly enhances the classification model’s overall performance and adaptability. A two-tier deep learning architecture is proposed for classification, consisting of a Convolutional Neural Network (CNN) and a modified Deep Neural Network (M-DNN). The CNN focuses on capturing temporal correlations within EEG data, while the M-DNN is designed to extract high-level spatial characteristics from selected EEG channels. Integrating optimal channel selection, hybrid optimization, and the two-tier deep learning methodology in our BCI framework presents an enhanced approach for precise and effective BCI control. Our model got 95.06% accuracy with high precision. This advancement has the potential to significantly impact neurorehabilitation and assistive technology applications, facilitating improved communication and control for individuals with motor impairments

Classification of Motor Imagery Tasks Derived from Unilateral Upper Limb based on a Weight-optimized Learning Model.

The accuracy of decoding fine motor imagery (MI) tasks remains relatively low due to the dense distribution of active areas in the cerebral cortex. To enhance the decoding of unilateral fine MI activity in the brain, a weight-optimized EEGNet model is introduced that recognizes six types of MI for the right upper limb, namely elbow flexion/extension, wrist pronation/supination and hand opening/grasping. The model is trained with augmented electroencephalography (EEG) data to learn deep features for MI classification. To address the sensitivity issue of the initial model weights to classification performance, a genetic algorithm (GA) is employed to determine the convolution kernel parameters for each layer of the EEGNet network, followed by optimization of the network weights through backpropagation. The algorithm's performance on the three joint classification is validated through experiment, achieving an average accuracy of 87.97%. The binary classification recognition rates for elbow joint, wrist joint, and hand joint are respectively 93.92%, 90.2%, and 94.64%. Thus, the product of the two-step accuracy value is obtained as the overall capability to distinguish the six types of MI, reaching an average accuracy of 81.74%. Compared to commonly used neural networks and traditional algorithms, the proposed method outperforms and significantly reduces the average error of different subjects. Overall, this algorithm effectively addresses the sensitivity of network parameters to initial weights, enhances algorithm robustness and improves the overall performance of MI task classification. Moreover, the method is applicable to other EEG classification tasks; for example, emotion and object recognition.

EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels

The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals. EEG source imaging (ESI) techniques can be applied to alleviate these issues and enhance the spatial segregation of information. Despite this potential solution, the use of ESI has not been extensively applied in BCI systems, largely due to accuracy concerns over reconstruction accuracy when using low-density EEG (ldEEG), which is commonly used in BCIs. To overcome these accuracy issues in low channel counts, recent studies have proposed reducing the number of EEG channels based on optimized channel selection. This work presents an evaluation of the spatial and temporal accuracy of ESI when applying optimized channel selection towards ldEEG number of channels. For this, a simulation study of source activity related to hand movement has been performed using as a starting point an EEG system with 339 channels. The results obtained after optimization show that the activity in the concerned areas can be retrieved with a spatial accuracy of 3.99, 10.69, and 14.29 mm (localization error) when using 32, 16, and 8 channel counts respectively. In addition, the use of optimally selected electrodes has been validated in a motor imagery classification task, obtaining a higher classification performance when using 16 optimally selected channels than 32 typical electrode distributions under 10–10 system, and obtaining higher classification performance when combining ESI methods with the optimal selected channels.

Shallow Inception Domain Adaptation Network for EEG-Based Motor Imagery Classification

Electroencephalography (EEG) data across multiple individuals have a high variance. Directly using the data to train a deep learning model usually degrades the performance. To address this issue, we propose a shallow Inception domain adaptation framework to extract informative deep features from data of multiple subjects for accurate motor imagery (MI) recognition. To our best knowledge, the Inception architecture in deep learning is combined with domain adaptation (DA) scheme for the first time for the MI classification task. The approach contains two compact Inception blocks that decode temporal features in different scales. In addition, we jointly optimize a novel combined loss function to reduce both marginal and class conditional discrepancies caused by the multi-modal structure of EEG signals. The DA-based loss enables Inception blocks to take full advantage of their learning abilities to capture discriminative patterns of MI data from multiple subjects instead of relying on the target user only. To demonstrate the effectiveness of our approach, we conduct substantial experiments on two wellknown datasets, BCI competition IV-2a and IV-2b. Results show that our model achieves better performance than state-of-theart strategies. The proposed model is able to extract informative features from high-variant EEG data collected from different individuals and achieves accurate MI classifications.

MAE-EEG-Transformer: A transformer-based approach combining masked autoencoder and cross-individual data augmentation pre-training for EEG classification

Convolutional neural networks (CNN) may not be ideal for extracting global temporal features from non-stationary Electroencephalogram (EEG) signals. The application of the masking-based method in EEG classification is not well studied, and there is a shortage of commonly accepted models for verifying inter-individual results in motor imagery classification tasks. The MAE-EEG-Transformer, a transformer with masking mechanism, is proposed in this article. It pre-trains by randomly masking signals and forces the model to learn semantic features. The pre-trained encoder module is fine-tuned and moved to the classification task to obtain the category of EEG signals. The effectiveness of features with and without pre-training is compared using t-SNE visualization to demonstrate pre-training’s inter-subject efficacy. The MAE EEG Transformer was extensively evaluated across three prevalent datasets in EEG-based motor imagery, demonstrating performance comparable to state-of-the-art models while requiring only approximately 20% of the computational cost (results in Table 1, 2, 3 and 4).

A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks.

The development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals. This paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task. The signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL. For Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm's performance was found to be optimal. The feature fusion of DTF network and graph theory features enhanced CSP algorithm's performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model's sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.

Semi-supervised multi-source transfer learning for cross-subject EEG motor imagery classification.

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.

Empowering EEG motor imagery classification with deep transfer learning approach

AbstractThe brain‐computer interface (BCI) enables individuals with impairments to interact with the real world without relying on the neuromuscular pathway. BCI leverages artificial intelligence (AI) models for control. It can capture brain activity patterns associated with mental processes and convert them into commands for actuators. One promising application of BCI is in rehabilitation centres. When compared to traditional methods integrated with machine learning (ML) approaches that were initially explored in the past decade, there is a relatively limited number of studies utilizing deep learning (DL) approaches in BCI applications. A novel approach has been developed for the automatic recognition of motor imagery (MI) tasks. This article introduces the improved sparrow search algorithm with deep transfer learning‐based EEG motor imagery classification (ISSADTL‐EEGMIC) algorithm for BCIs. The presented ISSADTL‐EEGMIC algorithm primarily focuses on the automated classification of motor imagery tasks. To achieve this, the ISSADTL‐EEGMIC method first preprocesses EEG signals using the wavelet packet decomposition (WPD) technique. Additionally, it employs the neural architecture search network (NASNet) technique for feature extraction. Moreover, the ISSADTL‐EEGMIC model performs the classification process using the stacked sparse autoencoder (SSAE) model. Furthermore, hyperparameter optimization of the SSAE model is performed using the ISSA technique, which helps achieve maximum classification performance. The results from the simulation of the ISSADTL‐EEGMIC algorithm have been examined using two benchmark datasets. The experimental findings suggest that it outperforms recent approaches in terms of performance.

Multiclass classification of motor imagery tasks based on multi-branch convolutional neural network and temporal convolutional network model.

Motor imagery (MI) is a cognitive process wherein an individual mentally rehearses a specific movement without physically executing it. Recently, MI-based brain-computer interface (BCI) has attracted widespread attention. However, accurate decoding of MI and understanding of neural mechanisms still face huge challenges. These seriously hinder the clinical application and development of BCI systems based on MI. Thus, it is very necessary to develop new methods to decode MI tasks. In this work, we propose a multi-branch convolutional neural network (MBCNN) with a temporal convolutional network (TCN), an end-to-end deep learning framework to decode multi-class MI tasks. We first used MBCNN to capture the MI electroencephalography signals information on temporal and spectral domains through different convolutional kernels. Then, we introduce TCN to extract more discriminative features. The within-subject cross-session strategy is used to validate the classification performance on the dataset of BCI Competition IV-2a. The results showed that we achieved 75.08% average accuracy for 4-class MI task classification, outperforming several state-of-the-art approaches. The proposed MBCNN-TCN-Net framework successfully captures discriminative features and decodes MI tasks effectively, improving the performance of MI-BCIs. Our findings could provide significant potential for improving the clinical application and development of MI-based BCI systems.

A time segment adaptive optimization method based on separability criterion and correlation analysis for motor imagery BCIs

Motor imagery (MI) plays a crucial role in brain-computer interface (BCI), and the classification of MI tasks using electroencephalogram (EEG) is currently under extensive investigation. During MI classification, individual differences among subjects in terms of response and time latency need to be considered. Optimizing the time segment for different subjects can enhance subsequent classification performance. In view of the individual differences of subjects in motor imagery tasks, this article proposes a Time Segment Adaptive Optimization method based on Separability criterion and Correlation analysis (TSAOSC). The fundamental principle of this method involves applying the separability criterion to various sizes of time windows within the training data, identifying the optimal raw reference signal, and adaptively adjusting the time segment position for each trial’s data by analyzing its relationship with the optimal reference signal. We evaluated our method on three BCI competition datasets, respectively. The utilization of the TSAOSC method in the experiments resulted in an enhancement of 4.90% in average classification accuracy compared to its absence. Additionally, building upon the TSAOSC approach, this study proposes a Nonlinear-TSAOSC method (N-TSAOSC) for analyzing EEG signals with nonlinearity, which shows improvements in the classification accuracy of certain subjects. The results of the experiments demonstrate that the proposed method is an effective time segment optimization method, and it can be integrated into other algorithms to further improve their accuracy.

Optimized Deep Learning Model Using Modified Whale’s Optimization Algorithm for EEG Signal Classification

Brain-Computer Interface (BCI) is a technology in which Electroencephalogram (EEG) signals are utilized to create a link between a person’s mental state and a computer-based signal processing system that decodes the signals without needing muscle movement. The mental process of picturing the movement of a body component without actually moving that body part is known as Motor Imagery (MI). MI BCI is a Motor Imagery-based Brain-Computer Interface that allows patients with motor impairments to interact with their environment by operating robotic prostheses, wheelchairs, and other equipment. Feature extraction and classification are essential parts of the EEG signal processing for MI BCI. In this work, Whales Optimization Algorithm with an Improved Mutualism Phase is proposed to find the optimal Convolutional Neural Network architecture for the classification of motor imagery tasks with high accuracy and less computational complexity. The Neurosky and BCI IV 2a datasets were used to evaluate the proposed methodology. Experiments demonstrate that the suggested technique outperforms other competing methods regarding classification accuracy values at 94.1% and 87.7% for the Neurosky and BCI datasets, respectively.

Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems

Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information. We designed an EEG based graph self-attention network (EGSAN) to learn low-dimensional embedding vector for EEG graph, which can be used as distinguishable features for motor imagery task classification. We evaluated our EGSAN model on two publicly available MI EEG datasets, each containing different types of motor imagery tasks. Our experiments demonstrate that our proposed model effectively extracts distinguishable features from EEG graphs, achieving significantly higher classification accuracies than existing state-of-the-art methods.

Priming cross-session motor imagery classification with a universal deep domain adaptation framework

Electroencephalogram (EEG) based motor imagery (MI) brain–computer interfaces (BCI) are widely used in applications related to rehabilitation and external device control. However, due to the non-stationary and low signal-to-noise ratio characteristics of EEG, classifying motor imagery tasks of the same participant from different recording sessions is generally challenging. Whether the classification accuracy of cross-session MI can be improved from the perspective of domain adaptation is a question worth verifying. In this paper, we propose a Siamese deep domain adaptation (SDDA) framework for cross-session MI classification based on mathematical models in domain adaptation theory. The SDDA framework primarily consists of three components: a novel preprocessing method based on domain-invariant features, a maximum mean discrepancy (MMD) loss for aligning source and target domain embedding features, and an improved cosine-based center loss designed to suppress the influence of noise and outliers on the neural network. The SDDA framework has been validated with two classic and popular convolutional neural networks (EEGNet and ConvNet) from BCI research field in two MI EEG public datasets (BCI Competition IV IIA, IIB). Compared with the vanilla EEGNet and ConvNet, the SDDA framework improves the MI classification accuracy by 10.49%, 7.60% respectively in IIA dataset, and 4.59%, 3.35% in IIB dataset. The SDDA not only significantly improves the classification performance of the vanilla networks but also surpasses state-of-the-art transfer learning methods, making it a superior and user-friendly approach for MI classification.

Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs.

In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the interaction and communication between the paralyzed patients and the outside world for moving and controlling external devices such as wheelchair and moving cursors. However, current approaches in the Motor Imagery-BCI system design require effective feature extraction methods and classification algorithms to acquire discriminative features from EEG signals due to the non-linear and non-stationary structure of EEG signals. This study investigates the effect of statistical significance-based feature selection on binary and multi-class Motor Imagery EEG signal classifications. In the feature extraction process performed 24 different time-domain features, 15 different frequency-domain features which are energy, variance, and entropy of Fourier transform within five EEG frequency subbands, 15 different time-frequency domain features which are energy, variance, and entropy of Wavelet transform based on five EEG frequency subbands, and 4 different Poincare plot-based non-linear parameters are extracted from each EEG channel. A total of 1,364 Motor Imagery EEG features are supplied from 22 channel EEG signals for each input EEG data. In the statistical significance-based feature selection process, the best one among all possible combinations of these features is tried to be determined using the independent t-test and one-way analysis of variance (ANOVA) test on binary and multi-class Motor Imagery EEG signal classifications, respectively. The whole extracted feature set and the feature set that contain statistically significant features only are classified in this study. We implemented 6 and 7 different classifiers in multi-class and binary (two-class) classification tasks, respectively. The classification process is evaluated using the five-fold cross-validation method, and each classification algorithm is tested 10 times. These repeated tests provide to check the repeatability of the results. The maximum of 61.86 and 47.36% for the two-class and four-class scenarios, respectively, are obtained with Ensemble Subspace Discriminant among all these classifiers using selected features including only statistically significant features. The results reveal that the introduced statistical significance-based feature selection approach improves the classifier performances by achieving higher classifier performances with fewer relevant components in Motor Imagery task classification. In conclusion, the main contribution of the presented study is two-fold evaluation of non-linear parameters as an alternative to the commonly used features and the prediction of multiple Motor Imagery tasks using statistically significant features.

Use of covariance matrix images for electroencephalography signal classification for multiclass motor imagery‐based brain computer interface

AbstractElectroencephalogram (EEG) signals can be used to control devices using brain computer interface (BCI) systems, which offer assistance to patients with neuromuscular diseases. This article aims to determine the feasibility of using spatial covariance matrices with deep convolutional neural networks for multiclass motor imagery (MI) task classification for EEG‐based BCIs. Covariance matrices derived from multichannel EEG data are converted into color‐scaled images, which are used to train the AlexNet neural network using transfer learning. Samples of 2‐s duration are extracted every 200 ms intervals from each MI activity EEG eventually increasing the training data size sixfold and reducing the latency in real‐time implementations. Observed the performance using two popular publicly available BCI datasets for two‐class and four‐class MI classification. BCI competition IV Dataset IIa has shown average accuracy of 93.99% (k = 0.88) for two‐class and 86.70% (k = 0.82) for four‐class MI classification. BCI competition III Dataset IVa exhibited average accuracy of 97.02% (k = 0.94) for two‐class classification. Good performance was observed for training with cross‐subject data for two‐class and four‐class classification. The proposed method has shown improved accuracy for multiclass MI classification compared with existing works. The CNN is able to extract features from the covariance matrix and classify the MI data with reasonable accuracy. Since this method can handle large‐dimension data, dimension reduction, and source separation techniques are not required.

MSATNet: multi-scale adaptive transformer network for motor imagery classification.

Motor imagery brain-computer interface (MI-BCI) can parse user motor imagery to achieve wheelchair control or motion control for smart prostheses. However, problems of poor feature extraction and low cross-subject performance exist in the model for motor imagery classification tasks. To address these problems, we propose a multi-scale adaptive transformer network (MSATNet) for motor imagery classification. Therein, we design a multi-scale feature extraction (MSFE) module to extract multi-band highly-discriminative features. Through the adaptive temporal transformer (ATT) module, the temporal decoder and multi-head attention unit are used to adaptively extract temporal dependencies. Efficient transfer learning is achieved by fine-tuning target subject data through the subject adapter (SA) module. Within-subject and cross-subject experiments are performed to evaluate the classification performance of the model on the BCI Competition IV 2a and 2b datasets. The MSATNet outperforms benchmark models in classification performance, reaching 81.75 and 89.34% accuracies for the within-subject experiments and 81.33 and 86.23% accuracies for the cross-subject experiments. The experimental results demonstrate that the proposed method can help build a more accurate MI-BCI system.

Deep CNN‐based classification of motor imagery tasks from EEG signals using 2D wavelet transformed images of adaptively reconstructed signals from MVMD decomposed modes

AbstractBrain–computer interfaces (BCI) have begun to revolutionize many aspects of human life, ranging from health to smart living and communication devices. Therefore, BCI technologies require accurate recognition and classification systems of the brain responses to a variety of motor imagery (MI) movements through electroencephalogram (EEG) signals. Nowadays, deep learning models have received a lot of interest for learning features and classifying numerous kinds of data. However, it has not been thoroughly investigated for EEG signal classification. In this study, to reach an improved performance in MI‐EEG signal classification, we proposed a new strategy based on continuous wavelet transform (CWT)‐based time‐frequency maps to generate two‐dimensional (2D) EEG images of adaptively reconstructed signals from the extracted multivariate mode decomposition (MVMD) modes. In the next step, the extracted signals are projected in the new space using the proposed multiclass common spatial pattern (MCCSP) filtering. The resulting images are then fed to the convolutional neural network (CNN) architectures (AlexNet and LeNet). The proposed framework has the benefit of achieving high classification accuracy to be attained even with a significant amount of input data. LeNet and AlexNet reach the best average accuracy rates of 95.33% and 93.66% on dataset 1 from BCI competition IV and the results on dataset 2a from competition IV are more promising than the current state of the arts. Our results depict that MI‐EEG task recognition using image classification approaches, along with CNNs, is as comparable or even superior to other existing traditional approaches and provides high potential for upcoming research.

A novel scheme based on information theory and transfer learning for multi classes motor imagery decoding

AbstractThe most important challenges of classifying Motor Imagery tasks based on the EEG signal are low signal‐to‐noise ratio, non‐stationarity, and the high subject dependence of the EEG signal. In this study, a framework for multi‐class decoding of Motor Imagery signals is presented. This framework is based on information theory and hybrid deep learning along with transfer learning. In this study, the OVR‐FBDiv method, which is based on the symmetric Kullback—Leibler divergence, is used to differentiate between features of different classes and highlight them. Then, the mRMR algorithm is used to select the most distinctive features obtained from the filters of symmetric KL divergence. Finally, a hybrid deep neural network consisting of CNN and LSTM is used to learn the spatial and temporal features of the EEG signal along with the transfer learning technique to overcome the problem of subject dependence in EEG signals. The average value of Kappa for the classification of 4‐class Motor Imagery data on BCI competition IV dataset 2a by the proposed method is 0.84. Also, the proposed method is compared with other state‐of‐the‐art methods.

Investigating Feature Selection Techniques to Enhance the Performance of EEG-Based Motor Imagery Tasks Classification

Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish communication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successful motor imagery (MI)-based BCI systems. However, they still face challenges in producing better performance with them because of the irrelevant features and high computational complexity. Selecting discriminative and relevant features to overcome the existing issues is crucial. In our proposed work, different feature selection algorithms have been studied to reduce the dimension of multiband feature space to improve MI task classification performance. In the procedure, we first decomposed the MI-based EEG signal into four sets of the narrowband signal. Then a common spatial pattern (CSP) approach was employed for each narrowband to extract and combine effective features, producing a high-dimensional feature vector. Three feature selection approaches, named correlation-based feature selection (CFS), minimum redundancy and maximum relevance (mRMR), and multi-subspace randomization and collaboration-based unsupervised feature selection (SRCFS), were used in this study to select the relevant and effective features for improving classification accuracy. Among them, the SRCFS feature selection approach demonstrated outstanding performance for MI classification compared to other schemes. The SRCFS is based on the multiple k-nearest neighbour graphs method for learning feature weight based on the Laplacian score and then discarding the irrelevant features based on the weight value, reducing the feature dimension. Finally, the selected features are fed into the support vector machines (SVM), linear discriminative analysis (LDA), and multi-layer perceptron (MLP) for classification. The proposed model is evaluated with two benchmark datasets, namely BCI Competition III dataset IVA and dataset IIIB, which are publicly available and mainly used to recognize the MI tasks. The LDA classifier with the SRCFS feature selection algorithm exhibits better performance. It proves the superiority of our proposed study compared to the other state-of-the-art BCI-based MI task classification systems.

Swin-TCNet: Swin-based temporal-channel cascade network for motor imagery iEEG signal recognition

Intracranial electroencephalography (iEEG) can record neuronal activities in different brain areas by placing strip or mesh electrodes on the surface of the brain. Benefiting from the high temporal and spatial resolution of iEEG signals, iEEG-based motor imagery has gained great attention, and made significant progress under the frameworks of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, both CNN- and RNN-based methods have limitations in perceiving the long-range global dependencies of local features in time and spatial dimensions. To exploit the temporal and spatial relationship of iEEG signals, we propose a Swin-Based Temporal Cascade Channel Network (Swin-TCNet) for motor imagery classification tasks, which is composed of three modules: Temporal-Swin (TS), Channel-Swin (CS) and Classifier. The TS and CS modules respectively extract the global dependencies of iEEG signals in the time and channel dimensions, and the Classifier assigns the class labels to the inputs. In addition, we introduce a Temporal-Channel Swin (TC-Swin) block instead of the standard Swin block to effectively reduce the computational complexity. We validate our model on the public imagined handwriting movement dataset collected by Stanford University. The experimental results demonstrate that our model can raise the classification accuracy by 2%–9% compared with 6 state-of-the-art models.