A Multi-Domain Convolutional Neural Network for EEG-Based Motor Imagery Decoding

Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network

Objective.Aiming for the research on the brain-computer interface (BCI), it is crucial to design a MI-EEG recognition model, possessing a high classification accuracy and strong generalization ability, and not relying on a large number of labeled training samples.Approach.In this paper, we propose a self-supervised MI-EEG recognition method based on self-supervised learning with one-dimensional multi-task convolutional neural networks and long short-term memory (1-D MTCNN-LSTM). The model is divided into two stages: signal transform identification stage and pattern recognition stage. In the signal transform recognition phase, the signal transform dataset is recognized by the upstream 1-D MTCNN-LSTM network model. Subsequently, the backbone network from the signal transform identification phase is transferred to the pattern recognition phase. Then, it is fine-tuned using a trace amount of labeled data to finally obtain the motion recognition model.Main results.The upstream stage of this study achieves more than 95% recognition accuracy for EEG signal transforms, up to 100%. For MI-EEG pattern recognition, the model obtained recognition accuracies of 82.04% and 87.14% with F1 scores of 0.7856 and 0.839 on the datasets of BCIC-IV-2b and BCIC-IV-2a.Significance.The improved accuracy proves the superiority of the proposed method. It is prospected to be a method for accurate classification of MI-EEG in the BCI system.

EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding

The motor imagery brain-computer interface (MI-BCI) based on electroencephalography (EEG) is a widely used human-machine interface paradigm. However, due to the non-stationarity and individual differences among subjects in EEG signals, the decoding accuracy is limited, affecting the application of the MI-BCI. In this paper, we propose the EISATC-Fusion model for MI EEG decoding, consisting of inception block, multi-head self-attention (MSA), temporal convolutional network (TCN), and layer fusion. Specifically, we design a DS Inception block to extract multi-scale frequency band information. And design a new cnnCosMSA module based on CNN and cos attention to solve the attention collapse and improve the interpretability of the model. The TCN module is improved by the depthwise separable convolution to reduces the parameters of the model. The layer fusion consists of feature fusion and decision fusion, fully utilizing the features output by the model and enhances the robustness of the model. We improve the two-stage training strategy for model training. Early stopping is used to prevent model overfitting, and the accuracy and loss of the validation set are used as indicators for early stopping. The proposed model achieves within-subject classification accuracies of 84.57% and 87.58% on BCI Competition IV Datasets 2a and 2b, respectively. And the model achieves cross-subject classification accuracies of 67.42% and 71.23% (by transfer learning) when training the model with two sessions and one session of Dataset 2a, respectively. The interpretability of the model is demonstrated through weight visualization method.

EEG-BCI-based motor imagery classification using double attention convolutional network

This article aims to improve and diversify signal processing techniques to execute a brain-computer interface (BCI) based on neurological phenomena observed when performing motor tasks using motor imagery (MI). The noise present in the original data, such as intermodulation noise, crosstalk, and other unwanted noise, is removed by Modify Least Mean Square (M-LMS) in the pre-processing stage. Traditional LMSs were unable to extract all the noise from the images. After pre-processing, the required features, such as statistical features, entropy features, etc., were extracted using Common Spatial Pattern (CSP) and Pearson's Correlation Coefficient (PCC) instead of the traditional single feature extraction model. The arithmetic optimization algorithm cannot select the features accurately and fails to reduce the feature dimensionality of the data. Thus, an Extended Arithmetic operation optimization (ExAo) algorithm is used to select the most significant attributes from the extracted features. The proposed model uses Double Attention Convolutional Neural Networks (DAttnConvNet) to classify the types of EEG signals based on optimal feature selection. Here, the attention mechanism is used to select and optimize the features to improve the classification accuracy and efficiency of the model. In EEG motor imagery datasets, the proposed model has been analyzed under class, which obtained an accuracy of 99.98% in class Baseline (B), 99.82% in class Imagined movement of a right fist (R) and 99.61% in class Imagined movement of both fists (RL). In the EEG dataset, the proposed model can obtain a high accuracy of 97.94% compared to EEG datasets of other models.