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Learning cross-regional dependence of EEG with convolutional neural networks for emotion classification
Seong-Eun Moon, Soobeom Jang, Jong-Seok Lee
Feb 12, 2018 (modified: Jun 04, 2018)ICLR 2018 Workshop Submissionreaders: everyoneShow Bibtex
Abstract:Electroencephalography (EEG) has received much attention because it provides comprehensive information of human perception and is relatively space- and cost-effective compared with the other functional brain imaging methods. In this paper, we present an approach to model undirected and directed cross-regional dependence of EEG signals with convolutional neural networks (CNNs). It considers the brain connectivity that measures simultaneous activation of different brain regions. Furthermore, the spatial arrangement of EEG electrodes is examined. We verify the effectiveness of the method for EEG-based emotion recognition.