Increasing the Stability of EEG-based Emotion Recognition with a Variant of Neural Processes

Yan-Kai Liu, Wei-Bang Jiang, Bao-Liang Lu

Abstract: Electroencephalography (EEG) signals provide an incredible promising access to decode human emotions in affective computing. Many approaches have been applied to building EEG-based affective models and much endeavor is made to improve the performance of affective models, where test data has similar quality as training data. However, due to the strong sensitivity of EEG to external factors such as body movement and electromagnetic interference, EEG signals usually have a lot of noise and subjects have to remain as motionless as possible in a quiet environment, which is difficult to be satisfied in real applications and severely influences the user experience. To deal with this problem, a common way is to drop those channels with intensive noise. However, this results in the loss of critical information and neither existing machine learning (ML) nor deep learning (DL) approaches can handle this situation well, especially when too many channels are missed. In this paper, we propose a robust variant of the neural processes model and evaluate the stability of our model under various circumstances to simulate random data corruption in real applications. We conduct two categories of experiments by controlling the number and the places of missing channels separately and compare with classical ML and DL models. The results demonstrate that the performance of our model significantly outperforms the existing models, even using only five channels. We also explore the critical brain regions in the current EEG electrode distribution. Final performance manifests our model has extreme stability in dealing with intractable situations and sheds light on the widespread usage of portable EEG-based affective computing.