  In this paper, we propose TSception, a multi-scale convolutional neural
network, to learn temporal dynamics and spatial asymmetry from affective
electroencephalogram (EEG). TSception consists of dynamic temporal, asymmetric
spatial, and high-level fusion Layers, which learn discriminative
representations in the time and channel dimensions simultaneously. The dynamic
temporal layer consists of multi-scale 1D convolutional kernels whose lengths
are related to the sampling rate of the EEG signal, which learns its dynamic
temporal and frequency representations. The asymmetric spatial layer takes
advantage of the asymmetric neural activations underlying emotional responses,
learning the discriminative global and hemisphere representations. The learned
spatial representations will be fused by a high-level fusion layer. With robust
nested cross-validation settings, the proposed method is evaluated on two
publicly available datasets DEAP and AMIGOS. And the performance is compared
with prior reported methods such as FBFgMDM, FBTSC, Unsupervised learning,
DeepConvNet, ShallowConvNet, and EEGNet. The results indicate that the proposed
method significantly (p<0.05) outperforms others in terms of classification
accuracy. The proposed methods can be utilized in emotion regulation therapy
for emotion recognition in the future. The source code can be found at:
https://github.com/deepBrains/TSception-New
