Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition

Miaoqi Pang · Hongtao Wang · Jiayang Huang · Chi‐Man Vong · Zhiqiang Zeng · Chuangquan Chen

37 citations0/0 files storedIEEE Transactions on Neural Systems and Rehabilitation Engineering

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Muscle activation and electromyography studies

Abstract

Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing subject-specific cross-session emotion recognition models. To simultaneously address these challenges, we propose a unified pre-training framework based on multi-scale masked autoencoders (MSMAE), which utilizes large-scale unlabeled EEG signals from multiple subjects and sessions to extract noise-robust, subject-invariant, and temporal-invariant features. We subsequently fine-tune the obtained generalized features with only a small amount of labeled data from a specific subject for personalization and enable cross-session emotion recognition. Our framework emphasizes: 1) Multi-scale representation to capture diverse aspects of EEG signals, obtaining comprehensive information; 2) An improved masking mechanism for robust channel-level representation learning, addressing missing channel issues while preserving inter-channel relationships; and 3) Invariance learning for regional correlations in spatial-level representation, minimizing inter-subject and inter-session variances. Under these elaborate designs, the proposed MSMAE exhibits a remarkable ability to decode emotional states from a different session of EEG data during the testing phase. Extensive experiments conducted on the two publicly available datasets, i.e., SEED and SEED-IV, demonstrate that the proposed MSMAE consistently achieves stable results and outperforms competitive baseline methods in cross-session emotion recognition.

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2018EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks 2018EmotionMeter: A Multimodal Framework for Recognizing Human Emotions 2013Differential entropy feature for EEG-based emotion classification 2017Emotions Recognition Using EEG Signals: A Survey 2017Identifying Stable Patterns over Time for Emotion Recognition from EEG 2020EEG-Based Emotion Recognition Using Regularized Graph Neural Networks 2009Emotion Recognition From EEG Using Higher Order Crossings 2017Emotion Recognition based on EEG using LSTM Recurrent Neural Network

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