  Affective computing is an important branch of artificial intelligence, and
with the rapid development of brain computer interface technology, emotion
recognition based on EEG signals has received broad attention. It is still a
great challenge to effectively explore the multi-dimensional information in the
EEG data in spite of a large number of deep learning methods. In this paper, we
propose a deep model called Attention-based Multiple Dimensions EEG Transformer
(AMDET), which can exploit the complementarity among the
spectral-spatial-temporal features of EEG data by employing the
multi-dimensional global attention mechanism. We transformed the original EEG
data into 3D temporal-spectral-spatial representations and then the AMDET would
use spectral-spatial transformer encoder layer to extract effective features in
the EEG signal and concentrate on the critical time frame with a temporal
attention layer. We conduct extensive experiments on the DEAP, SEED, and
SEED-IV datasets to evaluate the performance of AMDET and the results
outperform the state-of-the-art baseline on three datasets. Accuracy rates of
97.48%, 96.85%, 97.17%, 87.32% were achieved in the DEAP-Arousal, DEAP-Valence,
SEED, and SEED-IV datasets, respectively. We also conduct extensive experiments
to explore the possible brain regions that influence emotions and the coupling
of EEG signals. AMDET can perform as well even with few channels which are
identified by visualizing what learned model focus on. The accuracy could
achieve over 90% even with only eight channels and it is of great use and
benefit for practical applications.
