Dataset IIIa Research Articles

Classification of motor imagery EEG with ensemble RNCA model

Motor Imagery (MI) based brain-computer interface (BCI) systems are used for regaining the motor functions of neurophysiologically affected persons. But the performance of MI tasks is degraded due to the presence of redundant EEG channels. Hence, a novel ensemble regulated neighborhood component analysis (ERNCA) method provides a perfect identification of neural region that stimulate motor movements. Domains of statistical, frequency, spatial and transform-based features narrowed down the misclassification rate. The gradient boosting method selects the relevant features thereby reduces the computational complexity. Finally, Bayesian optimized ensemble classifier finetuned the classification accuracies of 97.22 % and 91.62 % for Datasets IIIa and IVa respectively. This approach is further strengthened by analyzing real-time data with the accuracy of 93.75 %. This method qualifies out of four benchmark methods with significant percent of improvement in accuracy for these three datasets. As per the spatial distribution of refined EEG channels, majority of the brain's motor functions concentrates on frontal and central cortex regions of brain.

A Hybrid Deep Learning Framework Using Scaling‐Basis Chirplet Transform for Motor Imagery EEG Recognition in Brain–Computer Interface Applications

ABSTRACTThe emerging field of brain–computer interface has significantly facilitated the analysis of electroencephalogram signals required for motor imagery classification tasks. However, the accuracy of EEG classification models has been restricted by the low signal‐to‐noise ratio, nonlinear nature of brain signals, and a lack of sufficient EEG data for training. To address these challenges, this study proposes a new approach that combines time‐frequency analysis with a hybrid parallel–series attention‐based deep learning network for EEG signal classification. The proposed framework comprises three main elements: first, a scaling‐basis chirplet transform designed to effectively capture the characteristics of nonstationary EEG signals; second, a hybrid parallel–series attention‐based deep learning network to extract features. The serial information flow continuously expands the receptive fields of output neurons, whereas parallel information flow extracts features based on different regions. Finally, machine learning classifiers are utilized to predict the corresponding motor imagery state. The developed EEG‐based motor imagery classification framework is assessed by two open‐source datasets, BCI competition III, dataset IIIa and BCI competition IV, dataset IIa and has achieved the average classification accuracy of 95.55% on BCI competition III, dataset IIIa and 90.18% on BCI competition IV, dataset IIa. The experimental findings illustrate that this study has attained promising motor imagery discrimination performance, surpassing existing techniques in terms of classification accuracy and kappa coefficient.

Adaptive Time-Frequency Segment Optimization for Motor Imagery Classification.

Motor imagery (MI)-based brain-computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time-frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time-frequency segments. In this study, we propose a novel method for optimizing time-frequency segments of MI-EEG using the sparrow search algorithm (SSA). Additionally, we apply a correlation-based channel selection (CCS) method that considers the correlation coefficient of features between each pair of EEG channels. Subsequently, we utilize a regularized common spatial pattern method to extract effective features. Finally, a support vector machine is employed for signal classification. The results on three BCI datasets confirmed that our algorithm achieved better accuracy (99.11% vs. 94.00% for BCI Competition III Dataset IIIa, 87.70% vs. 81.10% for Chinese Academy of Medical Sciences dataset, and 87.94% vs. 81.97% for BCI Competition IV Dataset 1) compared to algorithms with non-customized time-frequency segments. Our proposed algorithm enables adaptive optimization of EEG time-frequency segments, which is crucial for the development of clinically effective motor rehabilitation.

E‐CNNet: Time‐reassigned Multisynchrosqueezing transform‐based deep learning framework for MI‐BCI task classification

AbstractThe classification of electroencephalograms‐based motor imagery signals poses a significant issue in the design and development of brain‐computer interfaces. Neural Networks are observed to be successful in classification of motor imagery brain signals. However, the existing motor imagery data sets are of limited size and suffer from low signal to noise ratio. Hence, to achieve high performance with small datasets, this paper proposes a novel combination of time frequency analysis along with deep learning network to perform the brain signal classification task. The proposed framework consists of two parts: (1) Time‐reassigned Multisynchrosqueezing Transform to efficiently capture the dynamic properties of non‐stationary EEG; and (2) A new hybrid model E‐CNNet is proposed for feature extraction and classification of brain signals. A robust classification pipeline is constructed to classify the extracted features into respective motor imagery tasks. Ensemble of boosting classifiers is used to generate the final predictions, thus, reducing the variance and bias of individual classifiers. The performance of the proposed methodology is evaluated using two publicly available datasets: BCI competition III, dataset IIIa and BCI competition IV, dataset IIa. We have obtained the average classification accuracy of 94.44% on BCI competition III, dataset IIIa and 89.25% on BCI competition IV, dataset IIa.

Motor imagery classification method based on relative wavelet packet entropy brain network and improved lasso.

Motor imagery (MI) electroencephalogram (EEG) signals have a low signal-to-noise ratio, which brings challenges in feature extraction and feature selection with high classification accuracy. In this study, we proposed an approach that combined an improved lasso with relief-f to extract the wavelet packet entropy features and the topological features of the brain function network. For signal denoising and channel filtering, raw MI EEG was filtered based on an R2 map, and then the wavelet soft threshold and one-to-one multi-class score common spatial pattern algorithms were used. Subsequently, the relative wavelet packet entropy and corresponding topological features of the brain network were extracted. After feature fusion, mutcorLasso and the relief-f method were applied for feature selection, followed by three classifiers and an ensemble classifier, respectively. The experiments were conducted on two public EEG datasets (BCI Competition III dataset IIIa and BCI Competition IV dataset IIa) to verify this proposed method. The results showed that the brain network topology features and feature selection methods can retain the information of EEG more effectively and reduce the computational complexity, and the average classification accuracy for both public datasets was above 90%; hence, this algorithms is suitable in MI-BCI and has potential applications in rehabilitation and other fields.

Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns.

Motor imagery-based brain-computer interfaces (MI-BCIs) features are generally extracted from a wide fixed frequency band and time window of EEG signal. The performance suffers from individual differences in corresponding time to MI tasks. In order to solve the problem, in this study, we propose a novel method named Riemannian sparse optimization and Dempster-Shafer fusion of multi-time-frequency patterns (RSODSF) to enhance the decoding efficiency. First, we effectively combine the Riemannian geometry of the spatial covariance matrix with sparse optimization to extract more robust and distinct features. Second, the Dempster-Shafer theory is introduced and used to fuse each time window after sparse optimization of Riemannian features. Besides, the probabilistic values of the support vector machine (SVM) are obtained and transformed to effectively fuse multiple classifiers to leverage potential soft information of multiple trained SVM. The open-access BCI Competition IV dataset IIa and Competition III dataset IIIa are employed to evaluate the performance of the proposed RSODSF. It achieves higher average accuracy (89.7% and 96.8%) than state-of-the-art methods. The improvement over the common spatial patterns (SFBCSP) are respectively 9.9% and 12.4% (p < 0.01, paired t-test). These results show that our proposed RSODSF method is a promising candidate for the performance improvement of MI-BCI.

Riemannian distance based channel selection and feature extraction combining discriminative time-frequency bands and Riemannian tangent space for MI-BCIs

Objective. Motor imagery-based brain computer interfaces (MI-BCIs) have been widely researched because they do not demand external stimuli and have a high degree of maneuverability. In most scenarios, superabundant selected channels, fixed time windows, and frequency bands would certainly affect the performance of MI-BCIs due to the neurophysiological diversities among different individuals. In this study, we attempt to effectively use the Riemannian geometry of spatial covariance matrix to extract more robust features and thus enhance the decoding efficiency. Approach. First, we utilize a Riemannian distance-based electroencephalography (EEG) channel selection method, which preliminarily reduces the information redundancy in the first stage. Second, we extract discriminative Riemannian tangent space features of EEG signals of selected channels from the most discriminant time-frequency bands to further enhance decoding accuracy for MI-BCIs. Finally, we train a support vector machine model with a linear kernel to classify our extracted discriminative Riemannian features, and evaluate our proposed method using publicly available BCI Competition IV dataset Ⅰ (DS1) and Competition Ⅲ dataset Ⅲa (DS2). Main results. The experimental results show that the average classification accuracy with the selected 16-channel EEG signals of our method is 90.0% and 89.4% in DS1 and DS2 respectively. The average improvements are 20.0% and 21.2% on DS1, 9.4% and 7.2% on DS2 for 8 and 16 selected channels, respectively. Significance. These results show that our proposed method is a promising candidate for the performance improvement of MI-BCIs.

Whitening Technique Based on Gram-Schmidt Orthogonalization for Motor Imagery Classification of Brain-Computer Interface Applications.

A novel whitening technique for motor imagery (MI) classification is proposed to reduce the accuracy variance of brain–computer interfaces (BCIs). This method is intended to improve the electroencephalogram eigenface analysis performance for the MI classification of BCIs. In BCI classification, the variance of the accuracy among subjects is sensitive to the accuracy itself for superior classification results. Hence, with the help of Gram–Schmidt orthogonalization, we propose a BCI channel whitening (BCICW) scheme to minimize the variance among subjects. The newly proposed BCICW method improved the variance of the MI classification in real data. To validate and verify the proposed scheme, we performed an experiment on the BCI competition 3 dataset IIIa (D3D3a) and the BCI competition 4 dataset IIa (D4D2a) using the MATLAB simulation tool. The variance data when using the proposed BCICW method based on Gram–Schmidt orthogonalization was much lower (11.21) than that when using the EFA method (58.33) for D3D3a and decreased from (17.48) to (9.38) for D4D2a. Therefore, the proposed method could be effective for MI classification of BCI applications.

Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold.

Background: Recording the calibration data of a brain–computer interface is a laborious process and is an unpleasant experience for the subjects. Domain adaptation is an effective technology to remedy the shortage of target data by leveraging rich labeled data from the sources. However, most prior methods have needed to extract the features of the EEG signal first, which triggers another challenge in BCI classification, due to small sample sets or a lack of labels for the target. Methods: In this paper, we propose a novel domain adaptation framework, referred to as kernel-based Riemannian manifold domain adaptation (KMDA). KMDA circumvents the tedious feature extraction process by analyzing the covariance matrices of electroencephalogram (EEG) signals. Covariance matrices define a symmetric positive definite space (SPD) that can be described by Riemannian metrics. In KMDA, the covariance matrices are aligned in the Riemannian manifold, and then are mapped to a high dimensional space by a log-Euclidean metric Gaussian kernel, where subspace learning is performed by minimizing the conditional distribution distance between the sources and the target while preserving the target discriminative information. We also present an approach to convert the EEG trials into 2D frames (E-frames) to further lower the dimension of covariance descriptors. Results: Experiments on three EEG datasets demonstrated that KMDA outperforms several state-of-the-art domain adaptation methods in classification accuracy, with an average Kappa of 0.56 for BCI competition IV dataset IIa, 0.75 for BCI competition IV dataset IIIa, and an average accuracy of 81.56% for BCI competition III dataset IVa. Additionally, the overall accuracy was further improved by 5.28% with the E-frames. KMDA showed potential in addressing subject dependence and shortening the calibration time of motor imagery-based brain–computer interfaces.

Feature Selection Using Extreme Gradient Boosting Bayesian Optimization to upgrade the Classification Performance of Motor Imagery signals for BCI

BackgroundA motor imagery (MI) based brain computer interface (BCI) is a challenging nonmuscular connection system used to independently perform movement-related tasks. It is gaining increasing prominence in helping paralyzed individuals communicate with the real world. Achieving better classification accuracy is the major concern in the field of motor imagery-based BCI. To upgrade the classification performance, relevant features play a vital role. The relevant features can be selected by the extreme gradient Bayesian optimization (XGBO) method. MethodsIn this paper, a combination of time-, frequency-, and spatial-related MI features are extracted to design a reliable MI-BCI system. The proposed method incorporates the XGBO algorithm for feature selection and the random forest for the classification of EEG signals. The potency of the proposed system was investigated using two public EEG datasets (BCI Competition III dataset IIIa and dataset IVa). A novel XGBO algorithm increases the accuracy and reduces the time consumption by reducing the dimensionality of features. The proposed algorithm selects the minimum number of features that increase the computational efficacy for MI-based BCI systems. Comparison with existing methodsThe proposed method is compared with ANOVA, sequential forward selection, recursive feature elimination, and LASSO methods and the accuracy rate is increased with the lowest computation time. ResultsThe proposed method achieves mean accuracies of 94.44% and 88.72% and classification errors of 5.56% and 11.28% for Datasets IIIa and IVa, respectively. It outperforms four state-of-art methods with 0.87% and 0.59% increases in the accuracy for Datasets IIIa and IVa, respectively.

A spatial-frequency-temporal 3D convolutional neural network for motor imagery EEG signal classification

Motor imagery (MI) EEG signal classification is a critical issue for brain–computer interface (BCI) systems. In traditional MI EEG machine learning algorithms, feature extraction and classification often have different objective functions, thus resulting in information loss. To solve this problem, a novel spatial-frequency-temporal (SFT) 3D CNN model is proposed. Specifically, the energies of EEG signals located in multiple local SFT ranges are extracted to obtain a novel 3D MI EEG feature representation, and a novel 3D CNN model is designed to simultaneously learn the complex MI EEG features in the entire SFT domains and carry out classification. An extensive experimental study is implemented on two public EEG datasets to evaluate the effectiveness of our method. For BCI Competition III Dataset IVa, the average accuracy rate of five subjects obtained by the proposed method reaches 86.6% and yields 4.1% improvement over the state-of-the-art filter band common spatial pattern (FBCSP) method. For BCI Competition III dataset IIIa, by achieving an average accuracy rate of 91.85%, the proposed method outperforms the state-of-the-art dictionary pair learning (DPL) method by 4.44%.

A Novel Smart Motor Imagery Intention Human-Computer Interaction Model Using Extreme Learning Machine and EEG Signals.

The brain is the central nervous system that governs human activities. However, in modern society, more and more diseases threaten the health of the brain and nerves and spinal cord, making the human brain unable to conduct normal information interaction with the outside world. The rehabilitation training of the brain-computer interface can promote the nerve repair of the sensorimotor cortex in patients with brain diseases. Therefore, the research of brain-computer interface for motor imaging is of great significance for patients with brain diseases to restore motor function. Due to the characteristics of non-stationary, nonlinear, and individual differences of EEG signals, there are still many difficulties in the analysis and classification of EEG signals at this stage. In this study, the Extreme Learning Machine (ELM) model was used to classify motor-imaging EEG signals, identify the user’s intention, and control external devices. Considering that single-modal features cannot represent the core information, this study uses a fusion feature that combines temporal and spatial features as the final feature data. The fusion features are input to the trained ELM classifier, and the final classification result is obtained. Two sets of BCI competition data in the BCI competition public database are used to verify the validity of the model. The experimental results show that the ELM model has achieved a classification accuracy of 0.7832 in the classification task of Data Sets IIb, which is higher than other comparison algorithms, and shows universal applicability among different subjects. In addition, the average recognition rate of this model in the Data Sets IIIa classification task reaches 0.8347, which has obvious advantages compared with the comparative classification algorithm. The classification effect is smaller than the classification effect obtained by the champion algorithm of the same project, which has certain reference value.

An automatic subject specific channel selection method for enhancing motor imagery classification in EEG-BCI using correlation

A motor imagery (MI) based brain–computer interface (BCI) decodes the motor intention from the electroencephalogram (EEG) of a subject and translates this into a control signal. These intentions are hence classified as different cognitive tasks, e.g. left and right hand movements. A challenge in developing a BCI is handling the high dimensionality of the data recorded from multichannel EEG signals which are highly subject-specific. Designing a portable BCI whilst minimizing EEG channel number is a challenge. To this end, this paper presents a method to reduce the channel count with the goal of reducing computational complexity whilst maintaining a sufficient level of accuracy, by utilising an automatic subject-specific channel selection method created using the Pearson correlation coefficient. This method computes the correlation between EEG signals and helps to select highly correlated EEG channels for a particular subject without compromising classification accuracy (CA). Common spatial patterns (CSP) are used to analyse imagined left and right hand movements and the method is evaluated on both BCI Competition III Dataset IIIa and right hand and foot imagined tasks on BCI Competition III Dataset IVa. For both datasets, a minimum number of EEG channels are identified with an average channel reduction of 65.45% whilst demonstrating an increase of >5% in CA using channel Cz as a reference.

Time window and frequency band optimization using regularized neighbourhood component analysis for Multi-View Motor Imagery EEG classification

Spatial features optimized at frequency bands have been widely used in motor imagery (MI) based brain-computer interface (BCI) systems. However, using a fixed time window of electroencephalogram (EEG) to extract discriminatory features results in suboptimal MI classification performance because time latency during MI tasks is inconsistent between different subjects. Thus, apart from frequency band optimization, time window optimization is equally important to develop a subject-specific MI-BCI. With time windows, extracted feature space becomes a higher-order tensor problem that requires multi-view learning approaches to optimize features. This study proposes a novel multi-view feature selection method based on regularized neighbourhood component analysis to simultaneously optimize time windows and frequency bands. In the experiment, we extracted spatial features using common spatial patterns (CSP) from MI related EEG data at multiple time windows and frequency bands and optimized them using the proposed feature selection method. A support vector machine is trained to classify optimized CSP features to identify MI tasks. The proposed method achieved classification accuracies on three public BCI datasets (BCI competition IV dataset 2a, BCI competition III dataset IIIa, and BCI competition IV dataset 2b), which are 82.1 %, 91.7 %, and 84.5 %, respectively. Obtained results are superior to those obtained using standard competing algorithms. Hence, the proposed multi-view learning approach for simultaneous optimization of time windows and frequency bands of MI signals shows the potential to enhance a practical MI BCI device's performance.

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN.

Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier’s performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.

Neighborhood based decision theoretic rough set under dynamic granulation for BCI motor imagery classification

Brain Computer Interface is an interesting and important research field that has contributed widespread application systems. In the medical field, it is important for physically challenged persons to aid in rehabilitation and restoration. In Brain Computer Interface, computer acts as interface between brain signals and external device. The computer processes the brain signals and sends necessary instructions to external device. The external device helps in restoring the movement ability of patient. Motor imagery is the imagination of motor movements like hand, foot and tongue. There is an associated brain signal when the normal person moves their hand, foot and tongue. Similarly, there is an associated brain signal when the physically challenged person imagines moving their hand, foot and tongue. When this brain signal is analyzed by brain computer interface, it can facilitate motor movements through external device. The aim of this work is to analyze and classify the brain signals for motor movements to aid in rehabilitation and restoration. In this paper BCI Competition IV Dataset I, Dataset IIa, BCI Competition III Dataset IIIa and Neuroprosthetic EEG Dataset are analyzed A novel optimization technique with Neighborhood Decision Theoretic Rough Set under Dynamic Granulation is proposed for motor imagery classification. Neighborhood based Decision Theoretic Rough Set under Dynamic Granulation (NDTRS under DG) is hybrid approach combining two algorithms Neighborhood Rough Set and Decision Theoretic Rough Set under Dynamic Granulation ((DTRS under DG). Neighborhood Rough Set overcomes the drawback of discretization step in Rough Set. Decision Theoretic Rough Set under Dynamic Granulation algorithm has loss function for classification. The effectiveness of classification is improved since the loss function is involved in the construction of algorithm. The proposed method Neighborhood based Decision Theoretic Rough Set under Dynamic Granulation gives higher classification accuracy compared to existing approaches.

Motor Imagery EEG Spectral-Spatial Feature Optimization Using Dual-Tree Complex Wavelet and Neighbourhood Component Analysis

BackgroundFrequency band optimization improves the performance of common spatial pattern (CSP) in motor imagery (MI) tasks classification because MI-related electroencephalograms (EEGs) are highly frequency specific. Many variants of CSP algorithm divided the EEG into various sub bands and then applied CSP. However, the feature dimension of MI-EEG data increases with addition of frequency sub bands and requires efficient feature selection algorithms. The performance of CSP also depends on filtering techniques. MethodIn this study, we designed a dual tree complex wavelet transform based filter bank to filter the EEG into sub bands, instead of traditional filtering methods, which improved the spatial feature extraction efficiency. Further, after filtering EEG into different sub bands, we extracted spatial features from each sub band using CSP and optimized them by a proposed supervised learning framework based on neighbourhood component analysis (NCA). Subsequently, a support vector machine (SVM) is trained to perform classification. ResultsAn experimental study, conducted on two datasets (BCI Competition IV (Dataset 2b), and BCI competition III (Dataset IIIa)), validated the MI classification effectiveness of the proposed method in comparison with standard algorithms such as CSP, Filter bank CSP (CSP), and Discriminative FBCSP (DFBCSP). The average classification accuracy obtained by the proposed method for BCI Competition IV (Dataset 2b), and BCI Competition III (Dataset IIIa) are 84.02 ± 12.2 and 89.1 ± 7.50, respectively and found significant than that achieved by standard methods. ConclusionAchieved superior results suggest that the proposed algorithm can improve the performance of MI-based Brain-computer interface devices.

EEG Signal Classification Using Manifold Learning and Matrix‐Variate Gaussian Model

In brain-computer interface (BCI), feature extraction is the key to the accuracy of recognition. There is important local structural information in the EEG signals, which is effective for classification; and this locality of EEG features not only exists in the spatial channel position but also exists in the frequency domain. In order to retain sufficient spatial structure and frequency information, we use one-versus-rest filter bank common spatial patterns (OVR-FBCSP) to preprocess the data and extract preliminary features. On this basis, we conduct research and discussion on feature extraction methods. One-dimensional feature extraction methods like linear discriminant analysis (LDA) may destroy this kind of structural information. Traditional manifold learning methods or two-dimensional feature extraction methods cannot extract both types of information at the same time. We introduced the bilinear structure and matrix-variate Gaussian model into two-dimensional discriminant locality preserving projection (2DDLPP) algorithm and decompose EEG signals into spatial and spectral parts. Afterwards, the most discriminative features were selected through a weight calculation method. We tested the method on BCI competition data sets 2a, data sets IIIa, and data sets collected by our laboratory, and the results were expressed in terms of recognition accuracy. The cross-validation results were 75.69%, 70.46%, and 54.49%, respectively. The average recognition accuracy of new method is improved by 7.14%, 7.38%, 4.86%, and 3.8% compared to those of LDA, two-dimensional linear discriminant analysis (2DLDA), discriminant locality property projections (DLPP), and 2DDLPP, respectively. Therefore, we consider that the proposed method is effective for EEG classification.

Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification.

The common spatial patterns (CSP) algorithm is the most popular spatial filtering method applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain-computer interface (BCI) systems. The effectiveness of the CSP algorithm depends on optimal selection of the frequency band and time window from the EEG. Many algorithms have been designed to optimize frequency band selection for CSP, while few algorithms seek to optimize the time window. This study proposes a novel framework, termed common time-frequency-spatial patterns (CTFSP), to extract sparse CSP features from multi-band filtered EEG data in multiple time windows. Specifically, the whole MI period is first segmented into multiple subseries using a sliding time window approach. Then, sparse CSP features are extracted from multiple frequency bands in each time window. Finally, multiple support vector machine (SVM) classifiers with the Radial Basis Function (RBF) kernel are trained to identify the MI tasks and the voting result of these classifiers determines the final output of the BCI. This study applies the proposed CTFSP algorithm to three public EEG datasets (BCI competition III dataset IVa, BCI competition III dataset IIIa, and BCI competition IV dataset 1) to validate its effectiveness, compared against several other state-of-the-art methods. The experimental results demonstrate that the proposed algorithm is a promising candidate for improving the performance of MI-BCI systems.

A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding.

Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject's intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.