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An improved common spatial pattern combined with channel-selection strategy for electroencephalography-based emotion recognition.
Medical Engineering & Physics ( IF 2.2 ) Pub Date : 2020-05-19 , DOI: 10.1016/j.medengphy.2020.05.006
Mengmeng Yan 1 , Zhao Lv 2 , Wenhui Sun 3 , Ning Bi 4
Affiliation  

Emotional human-computer interaction (HCI) has become an important research area in the fields of artificial intelligence and cognitive science, owing to the requirement for active emotion perception. To enhance the performance of electroencephalography (EEG)-based emotional HCI, this paper proposes an improved common spatial pattern combined with a channel-selection strategy (ICSPCS) for EEG-based emotion recognition. Specifically, we first use a common spatial pattern algorithm to design a spatial domain filter according to three different emotions (positive, neutral, and negative). The traditional joint approximation diagonalization method using the criterion of the “highest score eigenvalue” may be unable to solve multiple classifications of emotion representation. Therefore, we design three different eigenvalue selection methods in terms of the positions of the eigenvalues with the highest scores. Finally, to make the installation easier and reduce the computational load, we also develop a channel-selection strategy based on the weight values that individually reflect the degrees of influence of all the channels on emotion recognition. Experiments are conducted on a self-collected dataset and on the MAHNOB-HCI dataset. The average recognition accuracies for the three emotion tasks are found to be 85.85% and 94.13% on the self-collected and MAHNOB-HCI datasets, respectively, thus proving the effectiveness of the proposed ICSPCS method for emotion recognition.



中文翻译:

一种改进的公共空间模式结合通道选择策略用于基于脑电图的情绪识别。

由于对主动情绪感知的需求,情绪人机交互(HCI)已成为人工智能和认知科学领域的重要研究领域。为了提高基于脑电图 (EEG) 的情绪 HCI 的性能,本文提出了一种改进的公共空间模式,结合通道选择策略 (ICSPCS) 进行基于 EEG 的情绪识别。具体来说,我们首先使用一种常见的空间模式算法,根据三种不同的情绪(积极、中性和消极)设计空间域过滤器。传统的以“最高分特征值”为准则的联合逼近对角化方法可能无法解决情感表征的多分类问题。因此,我们根据得分最高的特征值的位置设计了三种不同的特征值选择方法。最后,为了使安装更容易并减少计算负载,我们还开发了一种基于权重值的通道选择策略,这些权重值分别反映了所有通道对情绪识别的影响程度。实验是在自收集数据集和 MAHNOB-HCI 数据集上进行的。在自收集和 MAHNOB-HCI 数据集上,三个情感任务的平均识别准确率分别为 85.85% 和 94.13%,从而证明了所提出的 ICPCCS 方法在情感识别方面的有效性。我们还开发了一种基于权重值的通道选择策略,这些权重值分别反映了所有通道对情绪识别的影响程度。实验是在自收集数据集和 MAHNOB-HCI 数据集上进行的。在自收集和 MAHNOB-HCI 数据集上,三个情感任务的平均识别准确率分别为 85.85% 和 94.13%,从而证明了所提出的 ICPCCS 方法在情感识别方面的有效性。我们还开发了一种基于权重值的通道选择策略,这些权重值分别反映了所有通道对情绪识别的影响程度。实验是在自收集数据集和 MAHNOB-HCI 数据集上进行的。在自收集和 MAHNOB-HCI 数据集上发现三个情感任务的平均识别准确率分别为 85.85% 和 94.13%,从而证明了所提出的 ICPCCS 方法在情感识别方面的有效性。

更新日期:2020-05-19
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