Real-Time EEG-Based Emotion Recognition

Xiangkun Yu · Zhengjie Li · Zhibang Zang · Yinhua Liu

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Abstract

Most studies have demonstrated that EEG can be applied to emotion recognition. In the process of EEG-based emotion recognition, real-time is an important feature. In this paper, the real-time problem of emotion recognition based on EEG is explained and analyzed. Secondly, the short time window length and attention mechanisms are designed on EEG signals to follow emotion change over time. Then, long short-term memory with the additive attention mechanism is used for emotion recognition, due to timely emotion updates, and the model is applied to the SEED and SEED-IV datasets to verify the feasibility of real-time emotion recognition. The results show that the model performs relatively well in terms of real-time performance, with accuracy rates of 85.40% and 74.26% on SEED and SEED-IV, but the accuracy rate has not reached the ideal state due to data labeling and other losses in the pursuit of real-time performance.

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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 2013Differential entropy feature for EEG-based emotion classification 2017Identifying Stable Patterns over Time for Emotion Recognition from EEG 2011EEG-based emotion recognition during watching movies 2020A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition 2017Real-Time Movie-Induced Discrete Emotion Recognition from EEG Signals

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2024TorchEEGEMO: A deep learning toolbox towards EEG-based emotion recognition

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