PR-PL: A Novel Transfer Learning Framework with Prototypical Representation based Pairwise Learning for EEG-Based Emotion Recognition
This work addresses the challenge of generalizability and label ambiguity in affective brain-computer interfaces, which is incremental as it builds on existing transfer learning methods for EEG data.
The paper tackles the problem of individual differences and noisy labels in EEG-based emotion recognition by proposing a transfer learning framework with prototypical representation based pairwise learning, achieving state-of-the-art results across four cross-validation protocols on two benchmark databases.
Affective brain-computer interfaces based on electroencephalography (EEG) is an important branch in the field of affective computing. However, individual differences and noisy labels seriously limit the effectiveness and generalizability of EEG-based emotion recognition models. In this paper, we propose a novel transfer learning framework with Prototypical Representation based Pairwise Learning (PR-PL) to learn discriminative and generalized prototypical representations for emotion revealing across individuals and formulate emotion recognition as pairwise learning for alleviating the reliance on precise label information. Extensive experiments are conducted on two benchmark databases under four cross-validation evaluation protocols (cross-subject cross-session, cross-subject within-session, within-subject cross-session, and within-subject within-session). The experimental results demonstrate the superiority of the proposed PR-PL against the state-of-the-arts under all four evaluation protocols, which shows the effectiveness and generalizability of PR-PL in dealing with the ambiguity of EEG responses in affective studies. The source code is available at https://github.com/KAZABANA/PR-PL.