Reservoir Splitting method for EEG-based Emotion Recognition
Reservoir Splitting method for EEG-based Emotion Recognition
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DOI：
10.1109/bci57258.2023.10078629
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Anubhav;K. Fujiwara
中科院分区：
文献类型：
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作者：
Anubhav;K. Fujiwara
本文提出了一种新的水库分裂方法来训练一个有效的水库计算模型，情绪识别使用脑电信号（EEG）。由于不同的脑叶具有不同的功能，并且将这些功能组合在一起会导致最终的决定，因此我们建议将单个水库拆分为专用于不同脑叶的多个水库，并将它们整合以模仿人脑功能。我们利用公开的GAMEEMO数据集的EEG信号进行实验。将EEG信号输入到单个储库，并提出多个储库配置以获得表示。各种分类器：岭回归，支持向量机（SVM），梯度提升分类器（GBC），和随机森林从水库获得的表示进行训练。我们遵循Leave-One-Subject-Out（LOSO）策略来训练这些分类器。比较分类精度，我们注意到，从多个水库的建议模型的表示优于单水库模型，SVM比其他分类器的性能更好。此外，随着水库规模的增加，所有分类器的测试精度在效价和唤醒域都达到了一个峰值。建议的水库分裂方法可以扩展到探索不同的分裂配置，为未来的工作。
This paper presents a novel reservoir splitting method to train an efficient Reservoir Computing model for Emotion Recognition using Electroencephalogram (EEG) signals. Since different brain lobes have distinct functions and combining these functions results in the final decision, we propose splitting a single reservoir into multiple reservoirs dedicated to distinct lobes and integrating them to imitate the human brain functioning. We utilise the EEG signals from the publicly available GAMEEMO dataset for experiments. EEG signals are input to the single reservoir and proposed multiple reservoir configurations to obtain representations. Various classifiers: Ridge regression, Support Vector Machine (SVM), Gradient Boosted Classifier (GBC), and Random Forest are trained on the representations obtained from the reservoirs. We follow Leave-One-Subject-Out (LOSO) strategy to train these classifiers. Comparing the classification accuracy, we notice that representations from the proposed models of multiple reservoirs outperform the single reservoir model, and SVM performs better than the other classifiers. Furthermore, on increasing the reservoir size, the testing accuracy for all the classifiers attains a peak for both the Valence and Arousal domain. The proposed reservoir splitting method can be extended to explore diverse splitting configurations for future work.