Abstract
Brain-Computer Interface (BCI) has been a hot topic and an emerging technology in this decade. It is a communication tool between humans and systems using electroencephalography (EEG) to predicts certain aspects of cognitive state, such as attention or emotion. There are many types of sensors created to acquire the brain signal for different purposes. For example, the wet electrode is to obtain good quality, and the dry electrode is to achieve a wearable purpose. Hence, this paper investigates a comparative study of wet and dry systems using two cognitive tasks: attention experiment and music-emotion experiment. In attention experiments, a 3-back task is used as an assessment to measure attention and working memory. Comparatively, the music-emotion experiments are conducted to predict the emotion according to the user’s questionnaires. The proposed model is constructed by combining a shallow convolutional neural network (Shallow ConvNet) and a long short-term memory (LSTM) network to perform the feature extraction and classification tasks, respectively. This study further proposes transfer learning that focuses on utilizing knowledge acquired for the wet system and applying it to the dry system.
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Emsawas, T., Kimura, T., Fukui, Ki., Numao, M. (2020). Comparative Study of Wet and Dry Systems on EEG-Based Cognitive Tasks. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_28
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DOI: https://doi.org/10.1007/978-3-030-59277-6_28
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