Emotion Recognition using Multimodal Residual LSTM Network
Abstract
Various studies have shown that the temporal information captured by conventional long-short-term memory (LSTM) networks is very useful for enhancing multimodal emotion recognition using encephalography (EEG) and other physiological signals. However, the dependency among multiple modalities and high-level temporal-feature learning using deeper LSTM networks is yet to be investigated. Thus, we propose a multimodal residual LSTM (MMResLSTM) network for emotion recognition. The MMResLSTM network shares the weights across the modalities in each LSTM layer to learn the correlation between the EEG and other physiological signals. It contains both the spatial shortcut paths provided by the residual network and temporal shortcut paths provided by LSTM for efficiently learning emotion-related high-level features. The proposed network was evaluated using a publicly available dataset for EEG-based emotion recognition, DEAP. The experimental results indicate that the proposed MMResLSTM network yielded a promising result, with a classification accuracy of 92.87% for arousal and 92.30% for valence.
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Ma et al. and Huang et al. used EEG with other physiological signals and custom datasets, achieving accuracies of 92.87% and 82.75%, respectively.
“Studies by Ma et al. [14] and Huang et al. [15] used EEG with other physiological signals and custom datasets, achieving accuracies of 92.87% and 82.75%, respectively.”
From Multimodal Emotion Recognition: Emotion Classification Through the Integration of EEG and Facial Expressions (2025) · cites this paper as [14]
Cites
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EEG Based Emotion Recognition: A Tutorial and Review
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- Authors
- Ma, J., Tang, H., Zheng, W.-L., & Lu, B.-L.
- Affiliations
- Omron Sinic X Corporation (Japan) (JP)
- Shanghai Jiao Tong University (CN)
- Harvard University (US)
- Venue
- Proceedings of the 27th ACM International Conference on Multimedia
- Publisher
- ACM
- Published
- 2019-10-15
- Pages
- 176-183
- OpenAlex
- W2981372722
- Cited by
- 230
- References
- 5
- Citations by year
- 2026: 12 · 2025: 34 · 2024: 51 · 2023: 51 · 2022: 38 · 2021: 27 · 2020: 17
- FWCI
- 25.85
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Multimedia (ACM MM) (publishedVersion)
- Funders
- the National Key Research and Development Program of China (No. 2017YFB1002501)
- the Fundamental Research Funds for the Central Universities
- the National Natural Science Foundation of China (No. 61673266)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- emotion recognition, multimodal fusion, EEG, physiological signals, residual LSTM, temporal feature learning, valence and arousal classification, DEAP dataset
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