Motor Imagery Classification Research Articles

EEG-CDILNet: a lightweight and accurate CNN network using circular dilated convolution for motor imagery classification.

Objective.The combination of the motor imagery (MI) electroencephalography (EEG) signals and deep learning-based methods is an effective way to improve MI classification accuracy. However, deep learning-based methods often need too many trainable parameters. As a result, the trade-off between the network decoding performance and computational cost has always been an important challenge in the MI classification research.Approach.In the present study, we proposed a new end-to-end convolutional neural network (CNN) model called the EEG-circular dilated convolution (CDIL) network, which takes into account both the lightweight model and the classification accuracy. Specifically, the depth-separable convolution was used to reduce the number of network parameters and extract the temporal and spatial features from the EEG signals. CDIL was used to extract the time-varying deep features that were generated in the previous stage. Finally, we combined the features extracted from the two stages and used the global average pooling to further reduce the number of parameters, in order to achieve an accurate MI classification. The performance of the proposed model was verified using three publicly available datasets.Main results.The proposed model achieved an average classification accuracy of 79.63% and 94.53% for the BCIIV2a and HGD four-classification task, respectively, and 87.82% for the BCIIV2b two-classification task. In particular, by comparing the number of parameters, computation and classification accuracy with other lightweight models, it was confirmed that the proposed model achieved a better balance between the decoding performance and computational cost. Furthermore, the structural feasibility of the proposed model was confirmed by ablation experiments and feature visualization.Significance.The results indicated that the proposed CNN model presented high classification accuracy with less computing resources, and can be applied in the MI classification research.

An Evaluation of Hybrid Deep Learning Models for Classifying Multiple Lower Limb Actions.

Brain-computer Interfaces (BCIs) interpret electroencephalography (EEG) signals and translate them into control commands for operating external devices. The motor imagery (MI) paradigm is popular in this context. Recent research has demonstrated that deep learning models, such as convolutional neural network (CNN) and long short-term memory (LSTM), are successful in a wide range of classification applications. This is because CNN has the property of spatial invariance, and LSTM can capture temporal associations among features. A combination of CNN and LSTM could enhance the classification performance of EEG signals due to the complementation of their strengths. Such a combination has been applied to MI classification based on EEG. However, most studies focused on either the upper limbs or treated both lower limbs as a single class, with only limited research performed on separate lower limbs. We, therefore, explored hybrid models (different combinations of CNN and LSTM) and evaluated them in the case of individual lower limbs. In addition, we classified multiple actions: MI, real movements and movement observations using four typical hybrid models and aimed to identify which model was the most suitable. The comparison results demonstrated that no model was significantly better than the others in terms of classification accuracy, but all of them were better than the chance level. Our study informs the possibility of the use of multiple actions in BCI systems and provides useful information for further research into the classification of separate lower limb actions.

Kernel-Based Regularized EEGNet Using Centered Alignment and Gaussian Connectivity for Motor Imagery Discrimination

Brain–computer interfaces (BCIs) from electroencephalography (EEG) provide a practical approach to support human–technology interaction. In particular, motor imagery (MI) is a widely used BCI paradigm that guides the mental trial of motor tasks without physical movement. Here, we present a deep learning methodology, named kernel-based regularized EEGNet (KREEGNet), leveled on centered kernel alignment and Gaussian functional connectivity, explicitly designed for EEG-based MI classification. The approach proactively tackles the challenge of intrasubject variability brought on by noisy EEG records and the lack of spatial interpretability within end-to-end frameworks applied for MI classification. KREEGNet is a refinement of the widely accepted EEGNet architecture, featuring an additional kernel-based layer for regularized Gaussian functional connectivity estimation based on CKA. The superiority of KREEGNet is evidenced by our experimental results from binary and multiclass MI classification databases, outperforming the baseline EEGNet and other state-of-the-art methods. Further exploration of our model’s interpretability is conducted at individual and group levels, utilizing classification performance measures and pruned functional connectivities. Our approach is a suitable alternative for interpretable end-to-end EEG-BCI based on deep learning.

Improving single-hand open/close motor imagery classification by error-related potentials correction

ObjectiveThe ability of a brain-computer interface (BCI) to classify brain activity in electroencephalograms (EEG) during motor imagery (MI) tasks is an important performance indicator. Because the cortical regions that drive the single-handed open and closed tasks overlap, it is difficult to classify the EEG signals during executing both tasks. ApproachThe addition of special EEG features can improve the accuracy of classifying single-hand open and closed tasks. In this work, we designed a hybrid BCI paradigm based on error-related potentials (ErrP) and motor imagery (MI) and proposed a strategy to correct the classification results of MI by using ErrP information. The ErrP and MI features of EEG data from 11 subjects were superimposed. Main resultsThe corrected strategy improved the classification accuracy of single-hand open/close MI tasks from 52.3% to 73.7%, an increase of approximately 21%. SignificanceOur hybrid BCI paradigm improves the classification accuracy of single-hand MI by adding ErrP information, which provides a new approach for improving the classification performance of BCI.

A shallow mirror transformer for subject-independent motor imagery BCI

Objective:Motor imagery BCI plays an increasingly important role in motor disorders rehabilitation. However, the position and duration of the discriminative segment in an EEG trial vary from subject to subject and even trial to trial, and this leads to poor performance of subject-independent motor imagery classification. Thus, determining how to detect and utilize the discriminative signal segments is crucial for improving the performance of subject-independent motor imagery BCI. Approach:In this paper, a shallow mirror transformer is proposed for subject-independent motor imagery EEG classification. Specifically, a multihead self-attention layer with a global receptive field is employed to detect and utilize the discriminative segment from the entire input EEG trial. Furthermore, the mirror EEG signal and the mirror network structure are constructed to improve the classification precision based on ensemble learning. Finally, the subject-independent setup was used to evaluate the shallow mirror transformer on motor imagery EEG signals from subjects existing in the training set and new subjects. Main results:The experiments results on BCI Competition IV datasets 2a and 2b and the OpenBMI dataset demonstrated the promising effectiveness of the proposed shallow mirror transformer. The shallow mirror transformer obtained average accuracies of 74.48% and 76.1% for new subjects and existing subjects, respectively, which were highest among the compared state-of-the-art methods. In addition, visualization of the attention score showed the ability of discriminative EEG segment detection. This paper demonstrated that multihead self-attention is effective in capturing global EEG signal information in motor imagery classification. Significance:This study provides an effective model based on a multihead self-attention layer for subject-independent motor imagery-based BCIs. To the best of our knowledge, this is the shallowest transformer model available, in which a small number of parameters promotes the performance in motor imagery EEG classification for such a small sample problem.

A deep domain adaptation framework with correlation alignment for EEG-based motor imagery classification

It is impractical to collect sufficient and well-labeled EEG data in Brain-computer interface because of the time-consuming data acquisition and costly annotation. Conventional classification methods reusing EEG data from different subjects and time periods (across domains) significantly decrease the classification accuracy of motor imagery. In this paper, we propose a deep domain adaptation framework with correlation alignment (DDAF-CORAL) to solve the problem of distribution divergence for motor imagery classification across domains. Specifically, a two-stage framework is adopted to extract deep features for raw EEG data. The distribution divergence caused by subjected-related and time-related variations is further minimized by aligning the covariance of the source and target EEG feature distributions. Finally, the classification loss and adaptation loss are optimized simultaneously to achieve sufficient discriminative classification performance and low feature distribution divergence. Extensive experiments on three EEG datasets demonstrate that our proposed method can effectively reduce the distribution divergence between the source and target EEG data. The results show that our proposed method delivers outperformance (an average classification accuracy of 92.9% for within-session, an average kappa value of 0.761 for cross-session, and an average classification accuracy of 83.3% for cross-subject) in two-class classification tasks compared to other state-of-the-art methods.

A signal prediction-based method for motor imagery EEG classification

Motor imagery (MI)-based brain-computer interface (BCI) usually uses EEG signals recorded from numerous scalp electrodes for accurate MI classification, which requires long preparation time and high cost, hindering its practical application. A novel method based on signal prediction was proposed in this study to achieve high MI classification accuracy by using EEG recorded from only a small number of electrodes. The signal prediction model was built by using regression technique to estimate the full-channel EEG signals (e.g., recorded from 19, 22, and 29 electrodes) from the few-channel EEG signals on the central region, and then the predicted full-channel EEG signals were used for MI feature extraction and classification. The proposed methods based on multiple linear regression, ridge regression and extreme learning machine regression were compared with the traditional methods that directly used recorded few-channel and full-channel EEG signals, respectively, on three public EEG datasets. The proposed method significantly outperformed the traditional method that directly used few-channel EEG, and was comparable to or even superior to the traditional method that directly used full-channel EEG. The proposed method can accurately predict full-channel EEG and achieve high classification accuracy with only a small number of electrodes with fixed locations, which has great potential in building MI-based BCIs that meet the requirements of practical applications.

A convolutional spiking neural network with adaptive coding for motor imagery classification

Motor imagery (MI) signal classification is crucial for brain-computer interfaces (BCI). The third-generation neural network, spiking neural network (SNN), has rich neurodynamic properties in the spatiotemporal domain, and therefore it is more suitable for processing EEG signals. However, the feature extraction capability of the SNN previously applied to MI signal classification is limited by its structure, and the model’s classification accuracy is not comparable to the state-of-the-art algorithms. In this paper, we propose a spiking neural network model called SCNet, which combines the feature extraction capability of CNN with the biological interpretability of SNN, making the model structurally closer to the biological neuronal dynamical system and improving the classification accuracy. SCNet reduces information loss by adaptive coding with learnability and solves the training difficulties of spiking neural networks by surrogate gradient learning. We evaluated the performance of the proposed SCNet on three typically representative motor imagery datasets. The validation shows that the model outperforms state-of-the-art SNN-based MI classification methods and various ANN and machine learning methods. The experimental results demonstrate the generality and effectiveness of the proposed motor imagery EEG signal classification model. Better classification results can be obtained by designing a well-structured spiking neural network.

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.

Machine learning techniques for electroencephalogram based brain-computer interface: A systematic literature review

Brain-computer interface systems with Electroencephalogram (EEG), especially those use motor-imagery (MI) signals, have demonstrated the ability to control electromechanical devices with promising results. EEG being easy to record and non-invasive makes it a good choice for BCI systems. MI-based BCI systems compute neuronal activity and decipher these electrical impulses into gestures or effects, aiming to enable the person to communicate with their surroundings. This study summarises techniques of EEG signal processing used in the recent decade. This research paper presents an exhaustive survey on four aspects of EEG signals in BCI systems: signal acquisition, signal pre-processing, feature extraction, and classification. The most prominent time-frequency technique, wavelet transform (WT), and its updated version, wavelet packet transform (WPT), is primarily used in EEG-BCI systems for feature extraction. The development of artificial intelligence technology motivated researchers to classify motor imagery signals for BCI systems using machine learning (ML) and deep learning (DL) techniques. This literature survey paper explores more than 220 research papers related to ML and DL approaches to classify EEG signals for BCI systems. In order to identify prospective research areas for future investigation, present challenges are carefully considered, and suggestions are also provided for appropriate feature extraction and classification techniques. The authors expect that the investigation presented in this paper will help researchers to find accurate feature extraction, ML, and DL methods and these techniques will be supportive in devising an effective EEG-BCI system.

MI-CAT: A transformer-based domain adaptation network for motor imagery classification

Due to its convenience and safety, electroencephalography (EEG) data is one of the most widely used signals in motor imagery (MI) brain–computer interfaces (BCIs). In recent years, methods based on deep learning have been widely applied to the field of BCIs, and some studies have gradually tried to apply Transformer to EEG signal decoding due to its superior global information focusing ability. However, EEG signals vary from subject to subject. Based on Transformer, how to effectively use data from other subjects (source domain) to improve the classification performance of a single subject (target domain) remains a challenge. To fill this gap, we propose a novel architecture called MI-CAT. The architecture innovatively utilizes Transformer’s self-attention and cross-attention mechanisms to interact features to resolve differential distribution between different domains. Specifically, we adopt a patch embedding layer for the extracted source and target features to divide the features into multiple patches. Then, we comprehensively focus on the intra-domain and inter-domain features by stacked multiple Cross-Transformer Blocks (CTBs), which can adaptively conduct bidirectional knowledge transfer and information exchange between domains. Furthermore, we also utilize two non-shared domain-based attention blocks to efficiently capture domain-dependent information, optimizing the features extracted from the source and target domains to assist in feature alignment. To evaluate our method, we conduct extensive experiments on two real public EEG datasets, Dataset IIb and Dataset IIa, achieving competitive performance with an average classification accuracy of 85.26% and 76.81%, respectively. Experimental results demonstrate that our method is a powerful model for decoding EEG signals and facilitates the development of the Transformer for brain–computer interfaces (BCIs).

The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets.

The brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI. In this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario. The results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains.

Lightweight Source-Free Transfer for Privacy-Preserving Motor Imagery Classification

Transfer learning utilizes data or knowledge in one problem to help solve a related problem. It is particularly useful in electroencephalogram (EEG) based motor imagery (MI) classification, to handle high intra-subject and/or cross-subject variations. This paper considers offline unsupervised cross-subject MI classification, i.e., we have labeled EEG trials from several source subjects, but only unlabeled EEG trials from the target subject. Existing transfer learning approaches usually make use of the source domain data directly in target model learning. To protect the privacy of the source subjects, we propose lightweight source-free transfer (LSFT), which first generates source models locally and encapsulates them as model application programming interfaces (APIs), then constructs a virtual intermediate domain to transfer the knowledge in the source domains to the target domain, and finally performs feature adaptation learning. Compared with existing deep transfer learning approaches, LSFT does not need to transfer from massive source data or models, is computationally efficient, and has a small number of parameters. Experiments on four benchmark MI datasets demonstrated that LSFT outperformed 13 different approaches, including several state-of-the-art transfer learning approaches that make use of the source domain samples or model parameters directly.

Motor Imagery Classification Based on EEG Sensing with Visual and Vibrotactile Guidance.

Motor imagery (MI) is a technique of imagining the performance of a motor task without actually using the muscles. When employed in a brain-computer interface (BCI) supported by electroencephalographic (EEG) sensors, it can be used as a successful method of human-computer interaction. In this paper, the performance of six different classifiers, namely linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and three classifiers from the family of convolutional neural networks (CNN), is evaluated using EEG MI datasets. The study investigates the effectiveness of these classifiers on MI, guided by a static visual cue, dynamic visual guidance, and a combination of dynamic visual and vibrotactile (somatosensory) guidance. The effect of filtering passband during data preprocessing was also investigated. The results show that the ResNet-based CNN significantly outperforms the competing classifiers on both vibrotactile and visually guided data when detecting different directions of MI. Preprocessing the data using low-frequency signal features proves to be a better solution to achieve higher classification accuracy. It has also been shown that vibrotactile guidance has a significant impact on classification accuracy, with the associated improvement particularly evident for architecturally simpler classifiers. These findings have important implications for the development of EEG-based BCIs, as they provide valuable insight into the suitability of different classifiers for different contexts of use.

Bridging the BCI illiteracy gap: a subject-to-subject semantic style transfer for EEG-based motor imagery classification.

Brain-computer interfaces (BCIs) facilitate direct interaction between the human brain and computers, enabling individuals to control external devices through cognitive processes. Despite its potential, the problem of BCI illiteracy remains one of the major challenges due to inter-subject EEG variability, which hinders many users from effectively utilizing BCI systems. In this study, we propose a subject-to-subject semantic style transfer network (SSSTN) at the feature-level to address the BCI illiteracy problem in electroencephalogram (EEG)-based motor imagery (MI) classification tasks. Our approach uses the continuous wavelet transform method to convert high-dimensional EEG data into images as input data. The SSSTN 1) trains a classifier for each subject, 2) transfers the distribution of class discrimination styles from the source subject (the best-performing subject for the classifier, i.e., BCI expert) to each subject of the target domain (the remaining subjects except the source subject, specifically BCI illiterates) through the proposed style loss, and applies a modified content loss to preserve the class-relevant semantic information of the target domain, and 3) finally merges the classifier predictions of both source and target subject using an ensemble technique. We evaluate the proposed method on the BCI Competition IV-2a and IV-2b datasets and demonstrate improved classification performance over existing methods, especially for BCI illiterate users. The ablation experiments and t-SNE visualizations further highlight the effectiveness of the proposed method in achieving meaningful feature-level semantic style transfer.

Brain Computer Interface-Based Signal Processing Techniques for Feature Extraction and Classification of Motor Imagery Using EEG: A Literature Review

A communication path for people having severe neural disorders is provided by Brain Computer Interaction. The Brain–Computer Interface in an electroencephalogram is an important and challenging one for managing non-stationary EEG signals. EEG signals are more vulnerable to noise and artifacts. The Motor Imagery-based Brain–Computer Interface is used as a communication channel for people with neural disorders who have no muscular activity. For a well-established and accurate BCI system, two important steps have been used in MI-BCI, such as feature extraction and feature classification. Spectral methods and spatial methods are used for the feature extraction methods. The classifiers translate the features into the device commands. Linear Discriminant Analysis is the most widely used classification algorithm. So far, Support Vector Machine has been used as a classification method. In recent studies, Deep Neural Networks and Convolutional Neural Networks have been used. In this study, the feature extraction approaches as well as the signal classification methods for the motor imagery brain computer interface are thoroughly reviewed and presented.

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.

Stability of mental motor-imagery classification in EEG depends on the choice of classifier model and experiment design, but not on signal preprocessing.

Modern consciousness research has developed diagnostic tests to improve the diagnostic accuracy of different states of consciousness via electroencephalography (EEG)-based mental motor imagery (MI), which is still challenging and lacks a consensus on how to best analyse MI EEG-data. An optimally designed and analyzed paradigm must detect command-following in all healthy individuals, before it can be applied in patients, e.g., for the diagnosis of disorders of consciousness (DOC). We investigated the effects of two important steps in the raw signal preprocessing on predicting participant performance (F1) and machine-learning classifier performance (area-under-curve, AUC) in eight healthy individuals, that are based solely on MI using high-density EEG (HD-EEG): artifact correction (manual correction with vs. without Independent Component Analysis [ICA]), region of interest (ROI; motor area vs. whole brain), and machine-learning algorithm (support-vector machine [SVM] vs. k-nearest neighbor [KNN]). Results revealed no significant effects of artifact correction and ROI on predicting participant performance (F1) and classifier performance (AUC) scores (all ps > 0.05) in the SVM classification model. In the KNN model, ROI had a significant influence on the classifier performance [F(1,8.939) = 7.585, p = 0.023]. There was no evidence for artifact correction and ROI selection changing the prediction of participants performance and classifier performance in EEG-based mental MI if using SVM-based classification (71-100% correct classifications across different signal preprocessing methods). The variance in the prediction of participant performance was significantly higher when the experiment started with a resting-state compared to a mental MI task block [X2(1) = 5.849, p = 0.016]. Overall, we could show that classification is stable across different modes of EEG signal preprocessing when using SVM models. Exploratory analysis gave a hint toward potential effects of the sequence of task execution on the prediction of participant performance, which should be taken into account in future studies.

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.