The goal of this paper was to develop a novel method to track emotional processing in different brain regions using electroencephalogram (EEG) analysis. In addition, the role of EEG electrode selection and feature reduction in emotion recognition was investigated. To this end, the multi-channel EEG signals of 32 subjects available in DEAP dataset were studied. The best EEG electrode positions were selected based on lagged Poincare’s measures of EEG recordings and a source localization method (sLORETA). Three feature reduction algorithms, including random subset feature selection (RSFS), sequential floating forward selection (SFFS), and sequential forward selection (SFS) in combination with support vector machine (SVM), were evaluated to classify high/low valence and high/low arousal. The results showed that RSFS outperformed the other feature selection approaches. In addition, the positive impact of the EEG electrode selection on the classification performances has been confirmed. The most active EEG electrodes were FP1, C3, Cp1, P3, and Pz. Adopting RSFS and selected EEG electrodes, the mean subject-independent accuracies of 73.89 and 74.62% and subject-dependent accuracies of 98.97 and 98.94% were obtained for valence and arousal dimensions, respectively.

中文翻译：

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基于滞后庞加莱指数和sLORETA的自动情绪识别中的脑电电极选择新方法

本文的目的是开发一种使用脑电图（EEG）分析来跟踪不同大脑区域情绪处理的新方法。此外，研究了脑电电极选择和特征减少在情绪识别中的作用。为此，研究了DEAP数据集中32位受试者的多通道EEG信号。根据Poincare的EEG记录滞后量度和源定位方法（sLORETA），选择最佳的EEG电极位置。评估了三种特征约简算法，包括随机子集特征选择（RSFS），顺序浮动前向选择（SFFS）和顺序前向选择（SFS）与支持向量机（SVM）组合，以对高价/低价和高价/低价进行分类低唤醒。结果表明，RSFS优于其他特征选择方法。另外，已经证实了EEG电极选择对分类性能的积极影响。脑电图最活跃的电极是FP1，C3，Cp1，P3和Pz。采用RSFS和选定的EEG电极，分别在价位和觉醒尺寸方面获得了平均独立于受试者的准确度73.89和74.62％，以及独立于受试者的准确度98.97和98.94％。