Multi-class motor imagery EEG classification method with high accuracy and low individual differences based on hybrid neural network. (31st August 2021)

• Record Type: Journal Article
• Authors: Liu, Jinzhen; Ye, Fangfang; Xiong, Hui
• Is Part Of: Journal of neural engineering. Volume 18:Number 4(2021)
• Publication Date: 2021-08-31
• Subjects: motor imagery -- individual differences -- convolutional neural network -- gated recurrent unit -- brain-computer interface; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac1ed0
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective. Most current methods of classifying different patterns for motor imagery EEG signals require complex pre-processing and feature extraction steps, which consume time and lack adaptability, ignoring individual differences in EEG signals. It is essential to improve algorithm performance with the increased classes and diversity of subjects. Approach. This study introduces deep learning method for end-to-end learning to complete the classification of four-class MI tasks, aiming to improve the recognition rate and balance the classification accuracy among different subjects. A new one-dimensional input data representation method is proposed. This representation method can increase the number of samples and ignore the influence of channel correlation. In addition, a cascade network of convolutional neural network and gated recurrent unit is designed to learn time-frequency information from EEG data without extracting features manually, this model can capture the hidden representations related to different MI mode of each people. Main results . Experiments on BCI Competition 2a dataset and actual collected dataset achieve high accuracy near 99.40% and 92.56%, and the standard deviation is 0.34 and 1.35 respectively. Results demonstrate that the proposed method outperforms the advanced methods and baseline models. Significance. Experimental results show that the proposed method improves the accuracy of multi-classification and overcomes the impact of individual differences on classification by training neural network subject-dependent, which promotes the development of actual brain-computer interface systems.