Lew, W.-C. L., Wang, D., Shylouskaya, K., Zhang, Z., Lim, J.-H., Ang, K. K., & Tan, A.-H. (2020). EEG-based Emotion Recognition Using Spatial-Temporal Representation via Bi-GRU. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). https://doi.org/10.1109/embc44109.2020.9176682
Abstract:
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.
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Publisher Copyright
Funding Info:
There was no specific funding for the research done