Emotion recognition using brain wave signals involves using high dimensional electroencephalogram (EEG) data. In this paper, a window selection method based on mutual information is introduced to select an appropriate signal window to reduce the length of the signals. The motivation of the windowing method comes from EEG emotion recognition being computationally costly and the data having low signal-to-noise ratio. The aim of the windowing method is to find a reduced signal where the emotions are strongest. In this paper, it is suggested, that using only the signal section which best describes emotions improves the classification of emotions. This is achieved by iteratively comparing different-length EEG signals at different time locations using the mutual information between the reduced signal and emotion labels as criterion. The reduced signal with the highest mutual information is used for extracting the features for emotion classification. In addition, a viable framework for emotion recognition is introduced. Experimental results on publicly available datasets, DEAP and MAHNOB-HCI, show significant improvement in emotion recognition accuracy.

中文翻译：

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一种基于互信息的自适应加窗信息脑电图用于情绪识别

使用脑波信号的情绪识别涉及使用高维脑电图 (EEG) 数据。本文引入了一种基于互信息的窗口选择方法来选择合适的信号窗口来减少信号的长度。加窗方法的动机来自 EEG 情绪识别的计算成本高和数据的信噪比低。加窗方法的目的是找到情绪最强的减少信号。在本文中，建议仅使用最能描述情绪的信号部分来改进情绪的分类。这是通过使用减少的信号和情绪标签之间的互信息作为标准，在不同时间位置迭代比较不同长度的 EEG 信号来实现的。具有最高互信息的约简信号用于提取情感分类的特征。此外，还介绍了一个可行的情感识别框架。在公开可用的数据集 DEAP 和 MAHNOB-HCI 上的实验结果表明，情感识别准确度有显着提高。