Goshvarpour Atefeh, Goshvarpour Ateke
Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz 51335-1996, Iran.
Department of Biomedical Engineering, Imam Reza International University, Mashhad 91388-3186, Iran.
Brain Sci. 2023 May 4;13(5):759. doi: 10.3390/brainsci13050759.
Electroencephalogram (EEG) connectivity patterns can reflect neural correlates of emotion. However, the necessity of evaluating bulky data for multi-channel measurements increases the computational cost of the EEG network. To date, several approaches have been presented to pick the optimal cerebral channels, mainly depending on available data. Consequently, the risk of low data stability and reliability has increased by reducing the number of channels. Alternatively, this study suggests an electrode combination approach in which the brain is divided into six areas. After extracting EEG frequency bands, an innovative Granger causality-based measure was introduced to quantify brain connectivity patterns. The feature was subsequently subjected to a classification module to recognize valence-arousal dimensional emotions. A Database for Emotion Analysis Using Physiological Signals (DEAP) was used as a benchmark database to evaluate the scheme. The experimental results revealed a maximum accuracy of 89.55%. Additionally, EEG-based connectivity in the beta-frequency band was able to effectively classify dimensional emotions. In sum, combined EEG electrodes can efficiently replicate 32-channel EEG information.
脑电图（EEG）连接模式能够反映情绪的神经关联。然而，评估多通道测量的大量数据的必要性增加了EEG网络的计算成本。迄今为止，已经提出了几种方法来挑选最佳脑电通道，主要取决于可用数据。因此，通过减少通道数量，数据稳定性和可靠性低的风险增加了。或者，本研究提出了一种电极组合方法，即将大脑划分为六个区域。提取EEG频段后，引入了一种基于格兰杰因果关系的创新测量方法来量化脑连接模式。该特征随后被送入分类模块以识别效价-唤醒维度的情绪。使用生理信号进行情绪分析数据库（DEAP）作为基准数据库来评估该方案。实验结果显示最大准确率为89.55%。此外，基于EEG的β频段连接能够有效分类维度情绪。总之，组合式EEG电极能够有效地复制32通道EEG信息。
