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In Journal of neural engineering ; h5-index 52.0

OBJECTIVE : Deep transfer learning has been widely used to address the nonstationarity of electroencephalogram (EEG) data during motor imagery (MI) classification. However, previous deep learning approaches suffer from limited classification accuracy because the temporal and spatial features cannot be effectively extracted.

APPROACH : Here, we propose a novel end-to-end deep subject adaptation model (SACNN) to handle the problem of EEG-based MI classification. Our proposed model jointly optimizes three modules, i.e., a feature extractor, a classifier, and a subject adapter. Specifically, the feature extractor simultaneously extracts the temporal and spatial features from the raw EEG data using a parallel multiscale convolution network. In addition, we design a subject adapter to reduce the feature distribution shift between the source and target subjects by using the maximum mean discrepancy (MMD). By minimizing the classification loss and the distribution discrepancy, the model is able to extract the temporal-spatial features to the prediction of a new subject.

MAIN RESULTS : Extensive experiments are carried out on three EEG-based MI datasets, i.e., BCI Competition IV Dataset IIb, BCI Competition III Dataset IVa, and BCI Competition IV Dataset I, and the average accuracy reaches to 86.42%, 81.71% and 79.35% on the three datasets respectively. Furthermore, the statistical analysis also indicates the significant performance improvement of SACNN.

SIGNIFICANCE : This paper reveals the importance of the temporal-spatial features on EEG-based MI classification task. Our proposed SACNN model can make fully use of the temporal-spatial information to achieve the purpose.

Liu Siwei, Zhang Jia, Wang Andong, Wu Hanrui, Zhao Qibin, Long Jinyi


brain computer interface, deep learning, electroencephalogram, motor imagery, transfer learning