  The high temporal resolution and the asymmetric spatial activations are
essential attributes of electroencephalogram (EEG) underlying emotional
processes in the brain. To learn the temporal dynamics and spatial asymmetry of
EEG towards accurate and generalized emotion recognition, we propose TSception,
a multi-scale convolutional neural network that can classify emotions from 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 EEG, which learns the dynamic temporal and frequency representations of
EEG. The asymmetric spatial layer takes advantage of the asymmetric EEG
patterns for emotion, learning the discriminative global and hemisphere
representations. The learned spatial representations will be fused by a
high-level fusion layer. Using more generalized cross-validation settings, the
proposed method is evaluated on two publicly available datasets DEAP and
MAHNOB-HCI. The performance of the proposed network is compared with prior
reported methods such as SVM, KNN, FBFgMDM, FBTSC, Unsupervised learning,
DeepConvNet, ShallowConvNet, and EEGNet. TSception achieves higher
classification accuracies and F1 scores than other methods in most of the
experiments. The codes are available at https://github.com/yi-ding-cs/TSception
