Adaptive Domain Alignment Neural Networks for Cross-Domain EEG Emotion Recognition

Abstract

Electroencephalography (EEG) - based Emotion recognition is now facing great challenge of the intra- and inter-subject variability of EEG signal. Researchers attempted to handle this challenge by using transfer learning methods which usually share two main limitations: most of these methods align marginal distributions instead of conditional distributions of source and target data, making the alignment process classwise ambiguous; also, they prefer to use Multi-Layer Perceptron (MLP) with redundant parameters as classifiers, which is shown by recent research that could result serious over-fitting towards labeled data and prevent the model to draw a proper representation space. In our work, we propose a novel domain alignment method: Adaptive Domain Alignment Neural Networks (ADANN). Our method directly model conditional distributions of source and target domains by two sets of label-wise prototypes, representing the density maximum of each class, while the normalized correspond similarity naturally represents the conditional probability. The predicted label for a sample is given by the argument maxima of similarities and therefore the MLP classifier is not required. Using context-instance contrastive learning to align two sets of prototypes, their corresponding conditional distributions are being learned simultaneously. Exhaustive cross-domain experiments have been conducted under protocols that are strongly related to practical application scenarios and our proposed method achieves better or similar performance compared with recent state-of-the-art methods.