Cross-Subject EEG Emotion Recognition Based on Interconnected Dynamic Domain Adaptation

Electroencephalogram (EEG) is widely utilized in emotion recognition owing to its unique advantages. To achieve more optimal cross-subject emotion recognition, a cross subject emotion recognition method based on interconnection dynamic domain adaptation (IDDA) is proposed. In IDDA, dynamic graph convolution (DGC) is employed to dynamically learn the intrinsic relationships between different EEG channels and to extract domain invariant features. And dynamic domain adaptation (DDA) is employed to align the source domain and target domain, at the same time the emotional sub-domains is aligned, achieving more optimal cross subject emotion recognition. To select suitable subjects as the source domain, a multi-source selection algorithm is incorporated before dynamic adaptive computation reducing migration noise and achieving interconnection between DGC and DDA. IDDA enhances the emotion discrimination ability of domain invariant features, thereby improving the accuracy of cross-subject EEG emotion recognition. This method achieves classification results of 85.75% and 72.36% in cross subject experiments on SEED and SEED-IV.