2020/04/02 by Yi Ding, Ding, Yi, Neethu Robinson +11 · 3 citations
Computer Science · Engineering · Mathematics · Neuroscience · Psychology · #EEG and Brain-Computer Interfaces #Emotion and Mood Recognition #FOS: Computer and information sciences #FOS: Electrical engineering #Functional Brain Connectivity Studies #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Signal Processing (eess.SP) #cs.LG #eess.SP #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.2004.02965
Authors information updated only. Accepted to be published in: 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, July 19--24, 2020, part of 2020 IEEE World Congress on Computational Intelligence (IEEE WCCI 2020)
openalex publication_date 2020/04/02 · arxiv created 2020/04/08 · arxiv updated 2020/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we propose a deep learning framework, TSception, for emotion detection from electroencephalogram (EEG). TSception consists of temporal and spatial convolutional layers, which learn discriminative representations in the time and channel domains simultaneously. The temporal learner consists of multi-scale 1D convolutional kernels whose lengths are related to the sampling rate of the EEG signal, which learns multiple temporal and frequency representations. The spatial learner takes advantage of the asymmetry property of emotion responses at the frontal brain area to learn the discriminative representations from the left and right hemispheres of the brain. In our study, a system is designed to study the emotional arousal in an immersive virtual reality (VR) environment. EEG data were collected from 18 healthy subjects using this system to evaluate the performance of the proposed deep learning network for the classification of low and high emotional arousal states. The proposed method is compared with SVM, EEGNet, and LSTM. TSception achieves a high classification accuracy of 86.03%, which outperforms the prior methods significantly (p<0.05). The code is available at https://github.com/deepBrains/TSception