EEG-based Emotion Recognition Using Spatial-Temporal Representation via Bi-GRU

• Published in: Proceedings of the annual international conference of the IEEE Engineering in Medicine and Biology Society pp. 116 - 119
• Main Authors: Lew, Wai-Cheong Lincoln, Wang, Di, Shylouskaya, Katsiaryna, Zhang, Zhuo, Lim, Joo-Hwee, Ang, Kai Keng, Tan, Ah-Hwee
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2020
• ISSN: 1558-4615
• DOI: 10.1109/EMBC44109.2020.9176682

Many prior studies on EEG-based emotion recognition did not consider the spatial-temporal relationships among brain regions and across time. In this paper, we propose a Regionally-Operated Domain Adversarial Network (RODAN), to learn spatial-temporal relationships that correlate between brain regions and time. Moreover, we incorporate the attention mechanism to enable cross-domain learning to capture both spatial-temporal relationships among the EEG electrodes and an adversarial mechanism to reduce the domain shift in EEG signals. To evaluate the performance of RODAN, we conduct subject-dependent, subject-independent, and subject-biased experiments on both DEAP and SEED-IV data sets, which yield encouraging results. In addition, we also discuss the biased sampling issue often observed in EEG-based emotion recognition and present an unbiased benchmark for both DEAP and SEED-IV.