Dual-Threshold-Based Microstate Analysis on Characterizing Temporal Dynamics of Affective Process and Emotion Recognition From EEG Signals - Chen, Jing; Li, Haifeng; Ma, Lin; Bo, Hongjian; Soong, Frank; Shi, Yaohui (Frontiers in Neuroscience, 2021)
Recently, emotion classification from EEG data has attracted much attention. As EEG is unsteady rapidly changing voltage signal, the feature extracted from EEG usually changes dramatically, whereas emotion states change gradually. Most existing feature extraction approaches do not consider these differences between EEG and emotion. Microstate analysis could capture important spatio-temporal properties of EEG signal. At the same time, it could reduce the fast-changing EEG signals to a sequence of prototypical topographical maps. While microstate analysis has been widely used to study brain function, few studies have used this method to analyze how brain respond to emotional auditory stimuli. In this study, the authors proposed a novel feature extraction method based on EEG microstates for emotion recognition. Determining the optimal number of microstates automatically is a challenge for applying microstate analysis to emotion. This research proposed dual threshold-based Atomize and Agglomerate Hierarchical Clustering (DTAAHC) to determine the optimal number of microstate classes automatically. By using proposed method to model the temporal dynamics of auditory emotion process, we extracted microstates characteristics as novel temporospatial features to improve the performance of emotion recognition from EEG signals. We evaluated the proposed method on two datasets. For pubic music evoked EEG dataset DEAP, the microstate analysis identified ten microstates which together explained around 86% of the data in global field power peaks. The accuracy of emotion recognition achieved 75.8% in valence and 77.1% in arousal using microstate sequence characteristics as features. Compared to previous studies, the proposed method outperformed the current feature sets. For speech evoked EEG dataset, the microstate analysis identified nine microstates which together explained around 85% of the data. The accuracy of emotion recognition achieved 74.2% in valence and 72.3% in arousal using microstate sequence characteristics as features. The experimental results indicated that microstates characteristics can effectively improve the performance of emotion recognition from EEG signals.