PR-PL: A Novel Prototypical Representation Based Pairwise Learning Framework for Emotion Recognition Using EEG Signals

Rushuang Zhou · Zhiguo Zhang · Hong Chuan Fu · Li Zhang · Linling Li · Gan Huang +5 more

125 citations0/0 files storedIEEE Transactions on Affective Computing

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Gaze Tracking and Assistive Technology

Abstract

Affective brain-computer interface based on electroencephalography (EEG) is an important branch in the field of affective computing. However, the individual differences in EEG emotional data and the noisy labeling problem in the subjective feedback seriously limit the effectiveness and generalizability of existing models. To tackle these two critical issues, we propose a novel transfer learning framework with Prototypical Representation based Pairwise Learning (PR-PL). The discriminative and generalized EEG features are learned for emotion revealing across individuals and the emotion recognition task is formulated as pairwise learning for improving the model tolerance to the noisy labels. More specifically, a prototypical learning is developed to encode the inherent emotion-related semantic structure of EEG data and align the individuals' EEG features to a shared common feature space under consideration of the feature separability of both source and target domains. Based on the aligned feature representations, pairwise learning with an adaptive pseudo labeling method is introduced to encode the proximity relationships among samples and alleviate the label noises effect on modeling. Extensive results on two benchmark databases (SEED and SEED-IV) under four different cross-validation evaluation protocols validate the model reliability and stability across subjects and sessions. Compared to the literature, the average enhancement of emotion recognition across four different evaluation protocols is 2.04% (SEED) and 2.58% (SEED-IV). The source code is available athttps://github.com/KAZABANA/PR-PL.

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References

2015Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks 2018EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks 2018EmotionMeter: A Multimodal Framework for Recognizing Human Emotions 2020EEG-Based Emotion Recognition Using Regularized Graph Neural Networks 2018Spatial–Temporal Recurrent Neural Network for Emotion Recognition 2018A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition 2020A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition 2020An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals

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