Electroencephalography-based motor imagery classification using temporal convolutional network fusion. (August 2021)

• Record Type: Journal Article
• Authors: Musallam, Yazeed K.; AlFassam, Nasser I.; Muhammad, Ghulam; Amin, Syed Umar; Alsulaiman, Mansour; Abdul, Wadood; Altaheri, Hamdi; Bencherif, Mohamed A.; Algabri, Mohammed
• Is Part Of: Biomedical signal processing and control. Volume 69(2021)
• Publication Date: 2021-08
• Subjects: Motor imagery -- Electroencephalography (EEG) -- Temporal convolutional network; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102826
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An efficient TCNet-Fusion model for MI-EEG classification is proposed. 1D convolutions are applied in temporal domain and channel-wise in order. An image-like 2D representation is fed to the proposed model. The model achieved 83.73 % accuracy in BCI Competition IV-2a. The model achieved 94.41 % accuracy in High Gamma Dataset. Abstract: Motor imagery electroencephalography (MI-EEG) signals are generated when a person imagines a task without actually performing it. In recent studies, MI-EEG has been used in the rehabilitation process of paralyzed patients, therefore, decoding MI-EEG signals accurately is an important task, and it is difficult task due to the low signal-to-noise ratio and the variation of brain waves between subjects. Deep learning techniques such as the convolution neural network (CNN) have shown an impact in extracting meaningful features to improve the accuracy of classification. In this paper, we propose TCNet-Fusion, a fixed hyperparameter-based CNN model that utilizes multiple techniques, such as temporal convolutional networks (TCNs), separable convolution, depth-wise convolution, and the fusion of layers. This model outperforms other fixed hyperparameter-based CNN models while remaining similar to those that utilize variable hyperparameter networks, which are networks that change their hyperparameters based on each subject, resulting in higher accuracy than fixed networks. It also uses less memory than variable networks. The EEG signal undergoes two successive 1D convolutions, first along with the time domain, then channel-wise. Then, we obtain an image-like representation, which is fed to the main TCN. During experimentation, the model achieved a classification accuracy of 83.73 % on the four-class MI of the BCI Competition IV-2a dataset, and an accuracy of 94.41 % on the High Gamma Dataset.