BACKGROUND Emotion recognition is an increasingly important field of research in brain computer interactions. INTRODUCTION With the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Settling this issue will be a breakthrough of significance in the literature. METHODS The current study aims to identify the correlations between different emotions and brain regions with the help of suitable electrodes. Initially, independent component analysis algorithm is employed to remove artifacts and extract the independent components. The informative channels are then selected based on the thresholded average activity value for obtained components. Afterwards, effective features are extracted from selected channels common between all emotion classes. Features are reduced using the local subset feature selection method and then fed to a new classification model using modified Dempster-Shafer theory of evidence. RESULTS The presented method is employed to DEAP dataset and the results are compared to those of previous studies, which highlights the significant ability of this method to recognize emotions through electroencephalography, by the accuracy of about 91%. Finally, the obtained results are discussed and new aspects are introduced. CONCLUSIONS The present study addresses the long-standing challenge of finding neural correlates between human emotions and the activated brain regions. Also, we managed to solve uncertainty problem in emotion classification which is one of the most challenging issues in this field. The proposed method could be employed in other practical applications in future.

中文翻译：

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一种使用局部子集特征选择和改进的Dempster-Shafer理论进行情感识别的新方法。

背景技术情绪识别是脑计算机交互研究中越来越重要的领域。简介随着技术的进步，自动情感识别系统似乎不再遥不可及。尽管如此，检测情绪的神经相关性仍然是一个很大的瓶颈。解决这个问题将是文学意义上的重大突破。方法目前的研究旨在借助合适的电极来识别不同情绪与大脑区域之间的相关性。最初，采用独立成分分析算法去除伪影并提取独立成分。然后基于获得的成分的阈值平均活动值选择信息通道。之后，从所有情感类别之间共有的选定渠道中提取有效特征。使用局部子集特征选择方法对特征进行还原，然后使用改进的Dempster-Shafer证据理论将特征输入新的分类模型。结果提出的方法用于DEAP数据集，并将结果与​​以前的研究进行比较，从而突出了该方法通过脑电图识别情绪的显着能力，其准确度约为91％。最后，讨论了获得的结果并介绍了新的方面。结论本研究解决了寻找人类情绪与激活的大脑区域之间的神经相关性的长期挑战。也，我们设法解决了情绪分类中的不确定性问题，这是该领域最具挑战性的问题之一。所提出的方法将来可以在其他实际应用中使用。