Classification Of Motor Imagery EEG Signals Research Articles

Two Class Motor Imagery EEG Signal Classification for BCI Using LDA and SVM

Two Class Motor Imagery EEG Signal Classification for BCI Using LDA and SVM

Motor imagery EEG signal classification based on deformable convolution v3 and adaptive spatial attention mechanism

This paper is dedicated to solving the issue of insufficient feature extraction and declining model performance in the classification of motor imagery electroencephalogram (EEG) signals. We propose an enhanced ResNet18 neural network model and introduce two pivotal innovations at the model level. Firstly, we present the deformable convolution v3 (DCNv3), an improved convolutional structure that overcomes the limitations of the original deformable convolution. Through unbiased design with center sampling points, DCNv3 achieves more precise feature localization, enhances the model’s representation capability for complex structures, and reduces the risk of overfitting. Simultaneously, it comprehensively captures features in effective information regions, thereby strengthening the model’s generalization ability. Furthermore, this enhancement design also improves computational efficiency, reducing computational and parameter burdens. Secondly, we introduce a novel Adaptive Spatial Attention Mechanism (ASAM) aimed at precisely capturing feature correlations, thereby avoiding information loss. We conducted extensive comparative experiments on publicly available datasets BCI IV dataset 2a, BCI IV dataset 2b, and BCI III dataset 3a, achieving accuracy rates of 91.32%, 92.3%, and 90.47% respectively. Compared to other methods, our approach not only significantly improves the average classification accuracy but also demonstrates remarkable generalization capabilities. These results validate the effectiveness and stability of our method, thereby better meeting the practical application requirements of brain-computer interface technology.

Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO

A motor imagery brain–computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis.

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.

LSTM을 이용한 동작 상상 뇌파 분류

LSTM을 이용한 동작 상상 뇌파 분류

A novel recognition and classification approach for motor imagery based on spatio-temporal features.

Motor imagery, as a paradigm of brainmachine interfaces, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-machine interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.

Research on MI EEG signal classification algorithm using multi-model fusion strategy coupling

To enhance the accuracy of motor imagery(MI)EEG signal recognition, two methods, namely power spectral density and wavelet packet decomposition combined with a common spatial pattern, were employed to explore the feature information in depth in MI EEG signals. The extracted MI EEG signal features were subjected to series feature fusion, and the F-test method was used to select features with higher information content. Here regarding the accuracy of MI EEG signal classification, we further proposed the Platt Scaling probability calibration method was used to calibrate the results obtained from six basic classifiers, namely random forest (RF), support vector machines (SVM), Logistic Regression (LR), Gaussian naïve bayes (GNB), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). From these 12 classifiers, three to four with higher accuracy were selected for model fusion. The proposed method was validated on Datasets 2a of the 4th International BCI Competition, achieving an average accuracy of MI EEG data of nine subjects reached 91.46%, which indicates that model fusion was an effective method to improve classification accuracy, and provides some reference value for the research on MI brain-machine interface.

A parallel-hierarchical neural network (PHNN) for motor imagery EEG signal classification

Motor imagery brain-computer interfaces (MI-BCIs) play a crucial role in fields such as robot control and stroke rehabilitation. With the flourishing development of deep learning, there has been a continuous emergence of motor imagery deep learning models with higher decoding accuracy. Activation of specific brain regions, such as the motor regions in the frontal and parietal lobes, carries information about motor imagery. However, most studies only extract features from the entire brain region, neglecting the potential features of specific brain regions. This may lead to poorer decoding performance. Therefore, this article proposes a parallel-hierarchical neural network (PHNN), which implements a hierarchical approach to feature learning from brain region level to multi-level fusion. Learning at the brain region level mainly explores the key information of specific brain regions (region-level) and the overall features of the entire brain (global-level), to obtain region-level features (RLF) and global-level features (GLF). Furthermore, given that region-level features contain crucial information within specific brain regions, multi-level fusion is employed to capture and fully utilize the differences and connections between the RLF and GLF, resulting in more discriminative multi-level fusion features (MLFF). We evaluate the model performance on the publicly available BCI Competition IV-2a dataset and High Gamma dataset, achieving recognition accuracies of 84.67% and 94.02%, respectively.

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.

MBGA-Net: A multi-branch graph adaptive network for individualized motor imagery EEG classification

Background and objective: The development of deep learning has led to significant improvements in the decoding accuracy of Motor Imagery (MI) EEG signal classification. However, current models are inadequate in ensuring high levels of classification accuracy for an individual. Since MI EEG data is primarily used in medical rehabilitation and intelligent control, it is crucial to ensure that each individual’s EEG signal is recognized with precision. Methods: We propose a multi-branch graph adaptive network (MBGA-Net), which matches each individual EEG signal with a suitable time-frequency domain processing method based on spatio-temporal domain features. We then feed the signal into the relevant model branch using an adaptive technique. Through an enhanced attention mechanism and deep convolutional method with residual connectivity, each model branch more effectively harvests the features of the related format data. Results: We validate the proposed model using the BCI Competition IV dataset 2a and dataset 2b. On dataset 2a, the average accuracy and kappa values are 87.49% and 0.83, respectively. The standard deviation of individual kappa values is only 0.08. For dataset 2b, the average classification accuracies obtained by feeding the data into the three branches of MBGA-Net are 85.71%, 85.83%, and 86.99%, respectively. Conclusions: The experimental results demonstrate that MBGA-Net could effectively perform the classification task of motor imagery EEG signals, and it exhibits strong generalization performance. The proposed adaptive matching technique enhances the classification accuracy of each individual, which is beneficial for the practical application of EEG classification.

Convolutional neural network with support vector machine for motor imagery EEG signal classification

Convolutional neural network with support vector machine for motor imagery EEG signal classification

Motor imagery EEG signal classification with a multivariate time series approach

BackgroundElectroencephalogram (EEG) signals record electrical activity on the scalp. Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted, which compromises their classification accuracy. Achieving a reliable classification of motor imagery EEG signals opens the door to possibilities such as the assessment of consciousness, brain computer interfaces or diagnostic tools. We seek a method that works with a reduced number of variables, in order to avoid overfitting and to improve interpretability. This work aims to enhance EEG signal classification accuracy by using methods based on time series analysis. Previous work on this line, usually took a univariate approach, thus losing the possibility to take advantage of the correlation information existing within the time series provided by the different electrodes. To overcome this problem, we propose a multivariate approach that can fully capture the relationships among the different time series included in the EEG data. To perform the multivariate time series analysis, we use a multi-resolution analysis approach based on the discrete wavelet transform, together with a stepwise discriminant that selects the most discriminant variables provided by the discrete wavelet transform analysisResultsApplying this methodology to EEG data to differentiate between the motor imagery tasks of moving either hands or feet has yielded very good classification results, achieving in some cases up to 100% of accuracy for this 2-class pre-processed dataset. Besides, the fact that these results were achieved using a reduced number of variables (55 out of 22,176) can shed light on the relevance and impact of those variables.ConclusionsThis work has a potentially large impact, as it enables classification of EEG data based on multivariate time series analysis in an interpretable way with high accuracy. The method allows a model with a reduced number of features, facilitating its interpretability and improving overfitting. Future work will extend the application of this classification method to help in diagnosis procedures for detecting brain pathologies and for its use in brain computer interfaces. In addition, the results presented here suggest that this method could be applied to other fields for the successful analysis of multivariate temporal data.

Motor-Imagery EEG Signal Classification using Optimized Support Vector Machine by Differential Evolution Algorithm

Background: Motor-Imagery (MI) is a mental or cognitive stimulation without actual sensory input that enables the mind to represent perceptual information. This study aims to use the optimized support vector machine (OSVM) by differential evolution algorithm for motor-Imagery EEG signal classification. Methods: A total of three filters were applied to each signal during the preprocessing phase. The bandstop filter was used to remove urban noise and signal recorders, the median filter to remove random sudden peaks in the signal, and finally, the signal was normalized using the mapminmax filter. The most valuable features were extracted including mean signal intensity, minimum signal value, signal peak value, signal median, signal standard deviation, energy, corticoids, entropy, and signal skewness. Results: The accuracy of the SVM for linear, Gaussian, polynomial, and radial base kernels was 67.3%, 55.1%, 63.6%, and 55.1%, respectively, which was optimized after the classification model by differential evolution algorithm; however, the accuracy for OSVM was increased to 99.6%. Conclusion: Examination of the brain signal appearance for uniform motor-Imagery of both hands showed a significant difference between the signal of motor-Imagery mode with OSVM algorithm (99.6% accuracy), which gave promising results for classification motor imagery EEG signal.

A multiwavelet-based sparse time-varying autoregressive modeling for motor imagery EEG classification

Brain-computer Interface (BCI) system based on motor imagery (MI) heavily relies on electroencephalography (EEG) recognition with high accuracy. However, modeling and classification of MI EEG signals remains a challenging task due to the non-linear and non-stationary characteristics of the signals. In this paper, a new time-varying modeling framework combining multiwavelet basis functions and regularized orthogonal forward regression (ROFR) algorithm is proposed for the characterization and classification of MI EEG signals. Firstly, the time-varying coefficients of the time-varying autoregressive (TVAR) model are precisely approximated with the multiwavelet basis functions. Then a powerful ROFR algorithm is employed to dramatically alleviate the redundant model structure and accurately recover the relevant time-varying model parameters to obtain high resolution power spectral density (PSD) features. Finally, the features are sent to different classifiers for the classification task. To effectively improve the accuracy of classification, a principal component analysis (PCA) algorithm is utilized to determine the best feature subset and Bayesian optimization algorithm is performed to obtain the optimal parameters of the classifier. The proposed method achieves satisfactory classification accuracy on the public BCI Competition II Dataset III, which proves that this method potentially improves the recognition accuracy of MI EEG signals, and has great significance for the construction of BCI system based on MI.

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals

Over the past few years, the processing of motor imagery (MI) electroencephalography (EEG) signals has been attracted for developing brain-computer interface (BCI) applications, since feature extraction and classification of these signals are extremely difficult due to the inherent complexity and tendency to artifact properties of them. The BCI systems can provide a direct interaction pathway/channel between the brain and a peripheral device, hence the MI EEG-based BCI systems seem crucial to control external devices for patients suffering from motor disabilities. The current study presents a semi-supervised model based on three-stage feature extraction and machine learning algorithms for MI EEG signal classification in order to improve the classification accuracy with smaller number of deep features for distinguishing right- and left-hand MI tasks. Stockwell transform is employed at the first phase of the proposed feature extraction method to generate two-dimensional time–frequency maps (TFMs) from one-dimensional EEG signals. Next, the convolutional neural network (CNN) is applied to find deep feature sets from TFMs. Then, the semi-supervised discriminant analysis (SDA) is utilized to minimize the number of descriptors. Finally, the performance of five classifiers, including support vector machine, discriminant analysis, k-nearest neighbor, decision tree, random forest, and the fusion of them are compared. The hyperparameters of SDA and mentioned classifiers are optimized by Bayesian optimization to maximize the accuracy. The presented model is validated using BCI competition II dataset III and BCI competition IV dataset 2b. The performance metrics of the proposed method indicate its efficiency for classifying MI EEG signals.

A novel multi-branch hybrid neural network for motor imagery EEG signal classification

As a typical spontaneous brain-computer interface system, motor imagery has been widely used in areas such as robot control and stroke rehabilitation. Recently, researchers have started to study and propose various Convolutional Neural Network (CNN) structures based on motor imagery signals and have obtained better decoding accuracy compared with traditional machine learning algorithms. In this paper, we focus on a four-class motor imagery task. Therefore, we propose an end-to-end novel multi-branch hybrid neural network for motor imagery EEG signal classification. Notably, we divide the input signal into four frequency bands associated with the motor imagery signal. Meanwhile, we also introduced a Bidirectional Gated Recurrent Unit (BGRU) to identify EEG features. Nevertheless, it is extremely difficult to collect high-quality EEG data and the classification accuracy is degraded due to the strict requirements of the subjects and the experimental environment. To address this issue, we propose a novel data augmentation approach for frequency domain segmentation swap (Seg-Swap) for improving EEG motor imagery signal classification accuracy. We make use of the publicly available BCI IV 2a dataset and High Gamma dataset to evaluate the decoding performance of the model. The proposed multi-branch hybrid neural network (MBHNN) achieves 86.15% and 95.04% on the both datasets. Compared with several state-of-the-art (SOA) algorithms, our proposed model has a slight improvement in classification accuracy. Experimental results show that our proposed MBHNN has higher decoding classification and stronger robustness.

Motor imagery EEG classification algorithm based on improved lightweight feature fusion network

When deep learning techniques are introduced for Motor Imagery(MI) EEG signal classification, a multitude of state-of-the-art models, cannot be trained effectively because of the relatively small datasets. Proposing a model specialized for MI EEG signals classification plays a prominent role in promoting the combination of deep learning technology and MI EEG signal classification. In this paper, a novel Lightweight Feature Fusion Network(LFANN) based on an improved attention mechanism and tensor decomposition approach has been introduced. The proposed algorithm has been evaluated on a public benchmark dataset from BCI Competition IV, and the original dataset has been augmented with Enhance-Super-Resolution Generative Adversarial Network(ESRGAN). The experimental results demonstrate that the average accuracy of 91.58% and the average Kappa value of 0.881 can be achieved through the proposed algorithm. Furthermore, the compressed LAFFN, whose parameters have been compressed nearly ten times, creates no significant difference in performance compared to LAFFN. The investigation carried out through this experiment has provided novel insights into the classification research for MI EEG signals.

Motor imagery EEG signal classification using long short-term memory deep network and neighbourhood component analysis

Motor imagery EEG signal classification using long short-term memory deep network and neighbourhood component analysis

Motor imagery EEG classification algorithm based on CNN-LSTM feature fusion network

Motor imagery brain-computer interface (MI-BCI) provides a novel way for human-computer interaction. Traditional neural networks often use serial structure to extract spatial features when dealing with motor imagery EEG signal classification, ignoring temporal information and a large amount of available information in the middle layer, resulting in poor classification performance of MI-BCI. A neural network feature fusion algorithm is proposed by combining the convolutional neural network (CNN) and the long-short-term memory network (LSTM). Specifically, the CNN and LSTM are connected in parallel. The CNN extracts spatial features, the LSTM extracts temporal features and the flatten layer added after the convolutional layer extracts the middle layer features. Then all the features are merged in the fully connected layer to improve the accuracy of classification. The average accuracy and Kappa value of all subjects were 87.68% and 0.8245, respectively. The result shows that the feature fusion neural network proposed in this paper can effectively improve the accuracy of motor imagery EEG, and provides new ideas for the study of feature extraction and classification of motor imagery brain-computer interfaces.

Motor imagery EEG signal classification using image processing technique over GoogLeNet deep learning algorithm for controlling the robot manipulator

Controlling of a robotic arm using a brain-computer interface (BCI) is one of the most impressive applications. In this study, a novel method for the classification of motor imaging (MI) electroencephalography (EEG) signals are proposed for BCI. EEG signals are divided into three secondary tables, which were converted into spectrogram images. After applying the spectrogram method, the obtained images are divided into folder structures and deep learning is performed. In the deep learning stage, 400 images are obtained for each task as input to the GoogLeNet. After the deep learning completed, the presented system has been tested to imagine up, down, left and right movement to control the movement of the robot arm. It is observed that the robot arm performs the desired movement over 90% accuracy.