Abstract
Brain-computer interface (BCI) technologies enable direct communications between humans and computers by analyzing EEG signals. One of the challenges with traditional methods in classification tasks is receiving unsatisfactory recognition effects from EEG signals. In recent years, deep learning has drawn a great deal of attentions in diverse research fields, and could provide a novel solution for learning robust representations from EEG signals. In this chapter, we firstly introduce the basic concepts of deep learning techniques and two commonly used structures in time series analysis, namely, convolutional neural network and recurrent neural network. Then, we provide the applications of these two DL models to focus on the eye state detection task, which both achieve excellent recognition effects and are expected to be useful for broader applications in BCI systems.
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Gao, Z., Wang, X. (2019). Deep Learning. In: Hu, L., Zhang, Z. (eds) EEG Signal Processing and Feature Extraction. Springer, Singapore. https://doi.org/10.1007/978-981-13-9113-2_16
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DOI: https://doi.org/10.1007/978-981-13-9113-2_16
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Online ISBN: 978-981-13-9113-2
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