A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN.
A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN.
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深部细胞神经网络用于脑电信号情绪识别的窗口大小和通道设置的比较研究
DOI：
10.3390/s21051678
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发表时间：
2021-03-01
期刊：
影响因子：
--
通讯作者：
Kijsirikul B
中科院分区：
文献类型：
--
作者：
Keelawat P;Thammasan N;Numao M;Kijsirikul B
基于脑电信号的情感识别已成为一个活跃的研究领域。然而，仅使用脑电波识别情绪仍然非常具有挑战性，特别是独立于主体的任务。许多研究试图提出识别情绪的方法，包括卷积神经网络（CNN）等机器学习技术。由于CNN已经显示出它在推广到看不见的对象方面的潜力，因此操纵CNN超参数（如窗口大小和电极顺序）可能是有益的。据我们所知，这是第一个广泛观察CNN参数选择效果的工作。不同窗口大小的时间信息被发现会显着影响识别性能，并且CNN被发现比支持向量机对窗口大小的变化更敏感。对觉醒进行分类在窗口大小为10秒的情况下取得了最佳性能，获得了56.85%的准确度和0.1369的马修斯相关系数（MCC）。化合价识别具有最佳性能，窗口长度为8秒，准确率为73.34%，MCC值为0.4669。来自不同电极顺序的空间信息对分类的影响很小。总体而言，效价结果比唤醒结果具有更优越的上级性能，这可能是受左右半球之间大脑活动不对称性相关特征的影响。
Emotion recognition based on electroencephalograms has become an active research area. Yet, identifying emotions using only brainwaves is still very challenging, especially the subject-independent task. Numerous studies have tried to propose methods to recognize emotions, including machine learning techniques like convolutional neural network (CNN). Since CNN has shown its potential in generalization to unseen subjects, manipulating CNN hyperparameters like the window size and electrode order might be beneficial. To our knowledge, this is the first work that extensively observed the parameter selection effect on the CNN. The temporal information in distinct window sizes was found to significantly affect the recognition performance, and CNN was found to be more responsive to changing window sizes than the support vector machine. Classifying the arousal achieved the best performance with a window size of ten seconds, obtaining 56.85% accuracy and a Matthews correlation coefficient (MCC) of 0.1369. Valence recognition had the best performance with a window length of eight seconds at 73.34% accuracy and an MCC value of 0.4669. Spatial information from varying the electrode orders had a small effect on the classification. Overall, valence results had a much more superior performance than arousal results, which were, perhaps, influenced by features related to brain activity asymmetry between the left and right hemispheres.
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一种关注基于脑电图的情绪分类的分层双向 GRU 模型
影响因子：
3.9
作者：
Chen, J. X.;Jiang, D. M.;Zhang, N.
通讯作者：
Zhang, N.
影响因子：
11.2
作者：
Koelstra, Sander;Muhl, Christian;Patras, Ioannis (Yiannis)
通讯作者：
Patras, Ioannis (Yiannis)
使用 K 最近邻分类从多通道 EEG 信号中进行情绪识别
DOI：
10.3233/thc-174836
发表时间：
2018
期刊：
Technology and health care : official journal of the European Society for Engineering and Medicine
影响因子：
--
作者：
Li M;Xu H;Liu X;Lu S
通讯作者：
Lu S
影响因子：
3.8
作者：
Arjmand HA;Hohagen J;Paton B;Rickard NS
通讯作者：
Rickard NS
影响因子：
3.7
作者：
Jung, TP;Makeig, S;Sejnowski, TJ
通讯作者：
Sejnowski, TJ