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Temporal shift residual network for EEG-based emotion recognition: A 3D feature image sequence approach

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Abstract

In EEG-based emotion recognition, finding EEG representations that maintain both temporal and spatial features is crucial. This study aims to identify robust representations from EEG independent of subject differences and discriminative. We convert EEG data into feature image sequences with 3D representation, which fully preserve the spatial, spectral and temporal structure of the EEG signal. However, existing models ignore the complementarity between spatial-spectral-temporal features, which limits the classification ability of the models to some extent. Therefore, this paper proposes the Temporal Shift Residual Network(TSM-ResNet) based on feature image sequences for EEG emotion recognition. The Temporal Shift Module(TSM), a highly efficient and high-performance temporal modeling module, is utilized. It shifts certain channels of the feature map along the time dimension, facilitating information exchange between adjacent frames. In summary, the integration of feature image sequences, encompassing multi-domain information, and the powerful temporal modeling of TSM-ResNet enable the unified integration of spatial and spectral features while adequately considering temporal sequence features, all without increasing computational costs. The effectiveness of the proposed method is validated on the internationally recognized DEAP dataset, utilizing evaluation metrics such as accuracy, F1 score, and confusion matrix. The results from subject-dependent experiments (ten-fold cross-validation) demonstrate TSM-ResNet's average accuracy of 93.43% for valence and 93.26% for arousal. Additionally, excellent performance is achieved in subject-independent experiments (leave-one-subject-out cross-validation), with accuracy rates of 64.91% for valence and 62.52% for arousal. These findings highlight the advantages of the proposed method in cross-subject and within-subject emotion recognition.

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Data availability

Publicly available datasets were analyzed in this study. This data can be found at:

DEAPdataset http://www.eecs.qmul.ac.uk/mmv/datasets/deap/index.html.

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Funding statement

This work was supported by the National Natural Science Foundation of China (61300098), the Natural Science Foundation of Heilongjiang Province (F201347), and the Fundamental Research Funds for the Central Universities (2572015DY07).

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The authors declare they have no financial interests.

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Authors and Affiliations

  1. College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China

    Yu Chen & Haopeng Zhang

  2. College of Mecheanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China

    Jun Long & Yining Xie

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yu Chen, Haopeng Zhang, Jun Long and Yining Xie. The first draft of the manuscript was written by Haopeng Zhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yining Xie.

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Chen, Y., Zhang, H., Long, J. et al. Temporal shift residual network for EEG-based emotion recognition: A 3D feature image sequence approach. Multimed Tools Appl 83, 45739–45759 (2024). https://doi.org/10.1007/s11042-023-17142-7

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