超参数
计算机科学
卷积神经网络
脑电图
卷积(计算机科学)
人工智能
模式识别(心理学)
代表(政治)
运动表象
脑-机接口
人工神经网络
心理学
政治学
政治
精神科
法学
作者
Yazeed K. Musallam,Nasser I. AlFassam,Ghulam Muhammad,Syed Umar Amin,Mansour Alsulaiman,Wadood Abdul,Hamdi Altaheri,Mohamed A. Bencherif,Mohammed Algabri
标识
DOI:10.1016/j.bspc.2021.102826
摘要
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.
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