Motor Imagery Electroencephalography Research Articles

Nonlinear difference subspace method of motor imagery EEG classification in brain-computer interface

Noninvasive Motor Imagery (MI) electroencephalography (EEG) based brain-computer interface (BCI) systems are in high demand in both the medical and consumer sectors. However, the low signal-to-noise ratio of EEG and its nonlinear dynamics present challenges in processing. The effectiveness of information retrieval is directly related to the complexity of the algorithms, making BCI impractical for real-time applications. To enhance performance, this study introduced classification frameworks using nonlinear subspace and difference subspace methods, employing manifold learning techniques such as Kernel principal component analysis (KPCA) and Isometric feature mapping (Isomap). The optimal dimensions for the subspaces were selected using overlapping criteria in conjunction with the breadth-first-search (BFS) and depth-first-search (DFS) graph search methods. The proposed methods were evaluated on the BCI competition III and IV datasets using metrics including classification accuracy, Cohen's kappa coefficient, and F1-score. By exploiting the nonlinear characteristics of EEG and employing the geodesic distance between all pairs of trials, the modified Isomap called supervised discriminant Isomap projection (SD-ISoP) outperformed the KPCA-based methods and other recently proposed state-of-the-art methods. The results demonstrssate that the SD-ISoP-based difference subspace method achieved an average classification accuracy of 86.25% and 85.44% for BCI competition III and IV datasets, respectively.

Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification

Objective.Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG) data and ensuring its quality, insufficient training data often leads to overfitting and inadequate generalization capabilities of deep learning-based classification networks. Therefore, we propose a novel data augmentation method and deep learning classification model to enhance the decoding performance of MI-EEG further.Approach.The raw EEG signals were transformed into the time-frequency maps as the input to the model by continuous wavelet transform. An improved Wasserstein generative adversarial network with gradient penalty data augmentation method was proposed, effectively expanding the dataset used for model training. Additionally, a concise and efficient deep learning model was designed to improve decoding performance further.Main results.It has been demonstrated through validation by multiple data evaluation methods that the proposed generative network can generate more realistic data. Experimental results on the BCI Competition IV 2a and 2b datasets and the actual collected dataset show that classification accuracies are 83.4%, 89.1% and 73.3%, and Kappa values are 0.779, 0.782 and 0.644, respectively. The results indicate that the proposed model outperforms state-of-the-art methods.Significance.Experimental results demonstrate that this method effectively enhances MI-EEG data, mitigates overfitting in classification networks, improves MI classification accuracy, and holds positive implications for MI tasks.

Motor imagery electroencephalography channel selection based on deep learning: A shallow convolutional neural network

Electroencephalography (EEG) motor imagery (MI) signals have recently attracted much attention because of their potential to communicate with the surrounding environment in a specific way without the need for muscular and physical movement. Despite these advantages, these signals are difficult to detect due to their low signal to noise (SNR) rate and non-stationary and dynamic nature. Convolutional neural networks (CNN) can extract appropriate spatial and temporal features without the need for separate feature extraction and classification steps. Clean input data for CNN significantly improves its performance, but sometimes, common interference occurs between multi-channel signals. This challenge, along with the noisy EEG signals, degrades the performance of CNN networks. This paper presents a channel selection method based on convolutional neural networks to obviate these challenges. The proposed shallow convolutional neural network (SCNN) is designed with temporal convolution and pointwise convolution (using 1×1 convolution) to select the best channel with minimal computational load. Following the channel selection step, the multi-layer fusion CNN model was used to classify the signals. Choosing suitable and relevant EEG channels for motor imagery tasks makes it possible to create appropriate inputs for the classification model so that the model can be improved in the end. For High Gamma Dataset and Brian-Computer Interface (BCI) Competition IV-2a, the following accuracies were obtained: 81.15% and 72.01%, which are higher than when the other channel selections such as Mutual Information, Sequential Feature Forward Selection (SFFS) and wrapper methods are used.

A hybrid method for asynchronous detection of motor imagery electroencephalogram fusing alpha rhythm and movement-related cortical potential

The development of brain–computer interfaces (BCIs) enables direct human–computer interaction by real-time monitoring and translation of brain signals. Motor imagery electroencephalography (MI-EEG) systems, known for their non-invasiveness and user-friendliness, are particularly promising. Asynchronous systems, offering enhanced flexibility, represent the future of practical BCI applications. However, existing asynchronous detection methods in MI-EEG systems have yet to achieve satisfactory accuracy and latency. This paper proposes a hybrid asynchronous detection method that combines alpha rhythm changes and movement-related cortical potential (MRCP) features based on weighted Dempster–Shafer theory (AMAD-DS). The AMAD-DS method employs a hybrid architecture and two multi-domain joint analysis algorithms to process EEG signals from different areas: detecting alpha rhythm features in the occipital area and MRCP features in the sensorimotor area. The method fuses the results of these detections at the decision level using weighted D–S theory to produce the final output. Experiments conducted on a MI-EEG-based BCI system demonstrate that AMAD-DS outperformed methods using only MRCP or alpha rhythm features, improving the true positive rate by 12.6%, reducing the false positive rate by 0.5 FPs/min, and ensuring that the detection time of motor imagery onset is less than 500 ms. Online experiments further validate the method’s effectiveness, achieving a true positive rate of 91.1% and a false positive rate of 0.16 FPs/min.

Model and Data Dual-Driven Double-Point Observation Network for Ultra-Short MI EEG Classification.

Although deep networks have succeeded in various signal classification tasks, the time sequence samples used to train the deep models are usually required to reach a certain length. Especially, in brain computer interface (BCI) research, around 3.5s-long motor imagery (MI) Electroencephalography (EEG) samples are needed to obtain satisfactory classification performance. This time-span requirement of the training samples makes real-time MI BCI systems impossible to implement based on deep networks, which restricts the related researches within laboratory and makes practical application hard to accomplish. To address this issue, a double-point observation deep network (DoNet) is developed to classify ultra-short samples buried in noise. First, an analytical solution is developed theoretically to perform ultra-short signal classification based on double-point couples. Then, a signal-noise model is constructed to study the interference of noise on classification based on double-point couples. Based on which, an independent identical distribution condition is utilized to improve the classification accuracy in a data-driven manner. Combining the theoretical model and data-driven mechanism, DoNet can construct a steady data-distribution for the double-point couples of the samples with the same label. Therefore, the conditional probability of each double-point couple of a test sample can be obtained. With a voting strategy, the samples can be accurately classified by fusing these conditional probabilities. Meanwhile, the noise interference can be suppressed. DoNet has been evaluated on two public EEG datasets. Compared to most state-of-the-art methods, the 1s-long EEG signal classification accuracy has been improved by more than 3%.

A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network.

Motor imagery electroencephalography (EEG) signals have garnered attention in brain-computer interface (BCI) research due to their potential in promoting motor rehabilitation and control. However, the limited availability of labeled data poses challenges for training robust classifiers. In this study, we propose a novel data augmentation method utilizing an improved Deep Convolutional Generative Adversarial Network with Gradient Penalty (DCGAN-GP) to address this issue. We transformed raw EEG signals into two-dimensional time-frequency maps and employed a DCGAN-GP network to generate synthetic time-frequency representations resembling real data. Validation experiments were conducted on the BCI IV 2b dataset, comparing the performance of classifiers trained with augmented and unaugmented data. Results demonstrated that classifiers trained with synthetic data exhibit enhanced robustness across multiple subjects and achieve higher classification accuracy. Our findings highlight the effectiveness of utilizing a DCGAN-GP-generated synthetic EEG data to improve classifier performance in distinguishing different motor imagery tasks. Thus, the proposed data augmentation method based on a DCGAN-GP offers a promising avenue for enhancing BCI system performance, overcoming data scarcity challenges, and bolstering classifier robustness, thereby providing substantial support for the broader adoption of BCI technology in real-world applications.

Enhancing Motor Imagery Electroencephalography Classification with a Correlation-Optimized Weighted Stacking Ensemble Model

In the evolving field of Brain–Computer Interfaces (BCIs), accurately classifying Electroencephalography (EEG) signals for Motor Imagery (MI) tasks is challenging. We introduce the Correlation-Optimized Weighted Stacking Ensemble (COWSE) model, an innovative ensemble learning framework designed to improve MI EEG signal classification. The COWSE model integrates sixteen machine learning classifiers through a weighted stacking approach, optimizing performance by balancing the strengths and weaknesses of each classifier based on error correlation analysis and performance metrics evaluation across benchmark datasets. The COWSE model’s development involves selecting base classifiers, dynamically assigning weights according to performance, and employing a meta-classifier trained on these weighted predictions. Testing on the BNCI2014-002 dataset, the COWSE model achieved classification accuracy exceeding 98.16%, marking a significant advancement in MI EEG classification. This study highlights the potential of integrating multiple machine learning classifiers to address the complex challenges of EEG signal classification. By achieving new benchmarks and showcasing enhanced classification capabilities, the COWSE model contributes significantly to BCI research, encouraging further exploration into advanced ensemble learning strategies.

Noise-Factorized Disentangled Representation Learning for Generalizable Motor Imagery EEG Classification.

Motor Imagery (MI) Electroencephalography (EEG) is one of the most common Brain-Computer Interface (BCI) paradigms that has been widely used in neural rehabilitation and gaming. Although considerable research efforts have been dedicated to developing MI EEG classification algorithms, they are mostly limited in handling scenarios where the training and testing data are not from the same subject or session. Such poor generalization capability significantly limits the realization of BCI in real-world applications. In this paper, we proposed a novel framework to disentangle the representation of raw EEG data into three components, subject/session-specific, MI-task-specific, and random noises, so that the subject/session-specific feature extends the generalization capability of the system. This is realized by a joint discriminative and generative framework, supported by a series of fundamental training losses and training strategies. We evaluated our framework on three public MI EEG datasets, and detailed experimental results show that our method can achieve superior performance by a large margin compared to current state-of-the-art benchmark algorithms.

Selective multi–view time–frequency decomposed spatial feature matrix for motor imagery EEG classification

Decoding brain activity from non-invasive motor imagery electroencephalograph (MI-EEG) has garnered significant attentions for brain-computer interface (BCI) and brain disorders. Notably, owing to the remarkable advances in feature representation, extracting and selecting discriminative features in EEG decoding has gained widespread popularity in recent years. However, many EEG studies suffer from limited sample sizes with low signal-to-noise ratio, making it difficult to effectively represent complementary features in a single view. To address this fundamental limitation, this paper proposes a novel method to Selective extract the Multi-View Time-Frequency decomposed Spatial (S-MVTFS) feature matrix, which employs spatial features from Euclidean and Riemannian spaces based on time–frequency decompositions. It selectively extracts discriminative features on manifold embedded space and classifies feature matrix through sparse support matrix machine. The proposed method has been systematically benchmarked on three BCI competition MI-EEG datasets, and its classification performance surpasses several state-of-the-art methods. Notably, the S-MVTFS method achieved average classification improvements of 0.45%, 2.28%, and 2.04% on BCI-III dataset 4a, BCI-IV dataset 1, and BCI-IV dataset 2a, respectively. Moreover, it effectively captures meaningful temporal varying and spatially coupled features with parameter insensitivity. Our method therefore provides a novel MI-EEG tailored feature representation for decoding brain activity.

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.

Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification.

Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using deep learning have shown improved performance over conventional techniques. However, improving the classification accuracy on unseen subjects is still challenging due to intersubject variability, scarcity of labeled unseen subject data, and low signal-to-noise ratio (SNR). In this context, we propose a novel two-way few-shot network able to efficiently learn how to learn representative features of unseen subject categories and classify them with limited MI EEG data. The pipeline includes an embedding module that learns feature representations from a set of signals, a temporal-attention module to emphasize important temporal features, an aggregation-attention module for key support signal discovery, and a relation module for final classification based on relation scores between a support set and a query signal. In addition to the unified learning of feature similarity and a few-shot classifier, our method can emphasize informative features in support data relevant to the query, which generalizes better on unseen subjects. Furthermore, we propose to fine-tune the model before testing by arbitrarily sampling a query signal from the provided support set to adapt to the distribution of the unseen subject. We evaluate our proposed method with three different embedding modules on cross-subject and cross-dataset classification tasks using brain-computer interface (BCI) competition IV 2a, 2b, and GIST datasets. Extensive experiments show that our model significantly improves over the baselines and outperforms existing few-shot approaches.

Explainable cross-task adaptive transfer learning for motor imagery EEG classification

Objective. In the field of motor imagery (MI) electroencephalography (EEG)-based brain-computer interfaces, deep transfer learning (TL) has proven to be an effective tool for solving the problem of limited availability in subject-specific data for the training of robust deep learning (DL) models. Although considerable progress has been made in the cross-subject/session and cross-device scenarios, the more challenging problem of cross-task deep TL remains largely unexplored. Approach. We propose a novel explainable cross-task adaptive TL method for MI EEG decoding. Firstly, similarity analysis and data alignment are performed for EEG data of motor execution (ME) and MI tasks. Afterwards, the MI EEG decoding model is obtained via pre-training with extensive ME EEG data and fine-tuning with partial MI EEG data. Finally, expected gradient-based post-hoc explainability analysis is conducted for the visualization of important temporal-spatial features. Main results. Extensive experiments are conducted on one large ME EEG High-Gamma dataset and two large MI EEG datasets (openBMI and GIST). The best average classification accuracy of our method reaches 80.00% and 72.73% for OpenBMI and GIST respectively, which outperforms several state-of-the-art algorithms. In addition, the results of the explainability analysis further validate the correlation between ME and MI EEG data and the effectiveness of ME/MI cross-task adaptation. Significance. This paper confirms that the decoding of MI EEG can be well facilitated by pre-existing ME EEG data, which largely relaxes the constraint of training samples for MI EEG decoding and is important in a practical sense.

Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals

Objective. Brain–computer interfaces (BCIs) enable a direct communication pathway between the human brain and external devices, without relying on the traditional peripheral nervous and musculoskeletal systems. Motor imagery (MI)-based BCIs have attracted significant interest for their potential in motor rehabilitation. However, current algorithms fail to account for the cross-session variability of electroencephalography signals, limiting their practical application. Approach. We proposed a Riemannian geometry-based adaptive boosting and voting ensemble (RAVE) algorithm to address this issue. Our approach segmented the MI period into multiple sub-datasets using a sliding window approach and extracted features from each sub-dataset using Riemannian geometry. We then trained adaptive boosting (AdaBoost) ensemble learning classifiers for each sub-dataset, with the final BCI output determined by majority voting of all classifiers. We tested our proposed RAVE algorithm and eight other competing algorithms on four datasets (Pan2023, BNCI001-2014, BNCI001-2015, BNCI004-2015). Main results. Our results showed that, in the cross-session scenario, the RAVE algorithm outperformed the eight other competing algorithms significantly under different within-session training sample sizes. Compared to traditional algorithms that involved a large number of training samples, the RAVE algorithm achieved similar or even better classification performance on the datasets (Pan2023, BNCI001-2014, BNCI001-2015), even when it did not use or only used a small number of within-session training samples. Significance. These findings indicate that our cross-session decoding strategy could enable MI-BCI applications that require no or minimal training process.

Identifying a Suitable Signal Processing Technique for MI EEG Data

Motor imagery (MI) electroencephalography (EEG) technology is acquiring great attention from researchers due to its remarkable real-world applications. EEG signals inherit a high degree of non-stationarity, making their analysis not modest. Hence, choosing an appropriate signal processing approach becomes crucial. This comparative paper aims to identify a suitable signal processing method among famous approaches, namely short-time Fourier transform (STFT), continuous wavelet transform (CWT), and two variations of discrete wavelet transform maximal overlap DWT (MODWT) and MODWT multiresolution analysis (MODWTMRA). Different mother wavelet basis filters experimented with wavelet methods: Morse, Amor, Bump, Symlets, Daubechies, Coiflets, and Fejér-Korovkin. The different methods were tested on the classification of the right-hand and left-hand motor imagery tasks using the brain-computer interface (BCI) competition IV 2b dataset. A shallow convolutional neural network containing a single convolution layer was first trained and then used for classification. The experimental outcomes verified that MI EEG signals can be better analyzed and recognized using the maximal overlap-based signal processing methods. The classification accuracy proved that MODWT and MODWTMRA with the Symlets wavelet outperformed the other methods.

Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis.

Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain-computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG microstates with high spatiotemporal resolution and multichannel information can represent brain cognitive function. In this paper, four EEG microstates (MS1, MS2, MS3, MS4) were used in the analysis of the differences in the subjects' MI-BCI performance, and the four microstate feature parameters (the mean duration, the occurrences per second, the time coverage ratio, and the transition probability) were calculated. The correlation between the resting-state EEG microstate feature parameters and the subjects' MI-BCI performance was measured. Based on the negative correlation of the occurrence of MS1 and the positive correlation of the mean duration of MS3, a resting-state microstate predictor was proposed. Twenty-eight subjects were recruited to participate in our MI experiments to assess the performance of our resting-state microstate predictor. The experimental results show that the average area under curve (AUC) value of our resting-state microstate predictor was 0.83, and increased by 17.9% compared with the spectral entropy predictor, representing that the microstate feature parameters can better fit the subjects' MI-BCI performance than spectral entropy predictor. Moreover, the AUC of microstate predictor is higher than that of spectral entropy predictor at both the single-session level and average level. Overall, our resting-state microstate predictor can help MI-BCI researchers better select subjects, save time, and promote MI-BCI development.

Data-driven motor imagery EEG classifier using difference subspace method

Motor imagery (MI) electroencephalography (EEG) based brain-computer interface (BCI) systems are in great demand for many real-time applications both in the medical and consumer sectors. However, the processing of MI EEG data is challenging, and many of the approaches are complex and suffer from poor classification accuracy due to the unstability of EEG signals. To enhance the classification accuracy, a novel computationally efficient difference subspace method (DSM) of classification is proposed in this study. The proposed method is driven directly by the data without the necessity of extracting any handcrafted features. Subspaces are learned using principal component analysis (PCA). A search strategy based on overlapping criteria is used for selecting the subspace dimensions. The similarity between the two subspaces, measured based on the canonical angles, is used as the criteria to classify MI activity. The proposed method is implemented on BCI competition III and IV datasets, and the performance is evaluated on the basis of classification accuracy (%CA), Cohen’s kappa coefficient, F1-score etc., By retention of only the difference components between the two groups of data after removing the common components, the difference subspace method could do dominatingly well in classifying the left and right hand MI classification compared to the subspace method (SM) and many of state-of-the-art techniques. The results show that the DSM achieves an average classification accuracy and Cohen’s kappa coefficient for BCI competition III and IV datasets as 83.97% & 0.68 and 86.22% & 0.72, respectively.

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.

Multi-scale feature extraction and classification of motor imagery electroencephalography based on time series data enhancement

The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.

Deep Comparisons of Neural Networks from the EEGNet Family

A preponderance of brain–computer interface (BCI) publications proposing artificial neural networks for motor imagery (MI) electroencephalography (EEG) signal classification utilize one of the BCI Competition datasets. However, these databases encompass MI EEG data from a limited number of subjects, typically less than or equal to 10. Furthermore, the algorithms usually include only bandpass filtering as a means of reducing noise and increasing signal quality. In this study, we conducted a comparative analysis of five renowned neural networks (Shallow ConvNet, Deep ConvNet, EEGNet, EEGNet Fusion, and MI-EEGNet) utilizing open-access databases with a larger subject pool in conjunction with the BCI Competition IV 2a dataset to obtain statistically significant results. We employed the FASTER algorithm to eliminate artifacts from the EEG as a signal processing step and explored the potential for transfer learning to enhance classification results on artifact-filtered data. Our objective was to rank the neural networks; hence, in addition to classification accuracy, we introduced two supplementary metrics: accuracy improvement from chance level and the effect of transfer learning. The former is applicable to databases with varying numbers of classes, while the latter can underscore neural networks with robust generalization capabilities. Our metrics indicated that researchers should not disregard Shallow ConvNet and Deep ConvNet as they can outperform later published members of the EEGNet family.

Domain-independent short-term calibration based hybrid approach for motor imagery electroencephalograph classification: a comprehensive review

Domain-independent short-term calibration based hybrid approach for motor imagery electroencephalograph classification: a comprehensive review