Convolutional Neural Network Transfer Learning Applied to the Affective Auditory P300-Based BCI
1-33 Yayoicho, Inage-ku, Chiba-shi, Chiba 263-8522, Japan
**National Institute of Technology, Kagawa College
551 Kohda, Takuma-cho, Mitoyo-shi, Kagawa 769-1192, Japan
Brain-computer interface (BCI) enables us to interact with the external world via electroencephalography (EEG) signals. Recently, deep learning methods have been applied to the BCI to reduce the time required for recording training data. However, more evidence is required due to lack of comparison. To reveal more evidence, this study proposed a deep learning method named time-wise convolutional neural network (TWCNN), which was applied to a BCI dataset. In the evaluation, EEG data from a subject was classified utilizing previously recorded EEG data from other subjects. As a result, TWCNN showed the highest accuracy, which was significantly higher than the typically used classifier. The results suggest that the deep learning method may be useful to reduce the recording time of training data.
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