Dual-Threshold-Based Microstate Analysis on Characterizing Temporal Dynamics of Affective Process and Emotion Recognition From EEG Signals

Abstract

Recently, emotion classification from electroencephalogram (EEG) data has attracted much attention. As EEG is an unsteady and rapidly changing voltage signal, the features extracted from EEG usually change 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 signals. 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 responds 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 the proposed method to model the temporal dynamics of auditory emotion process, we extracted microstate characteristics as novel temporospatial features to improve the performance of emotion recognition from EEG signals. We evaluated the proposed method on two datasets. For public music-evoked EEG Dataset for Emotion Analysis using Physiological signals, the microstate analysis identified 10 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 the 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 microstate characteristics can effectively improve the performance of emotion recognition from EEG signals.

DEEMD-SPP: A Novel Framework for Emotion Recognition Based on EEG Signals

“…Speech carries emotional information in human communication. In this article we consider a dataset collected from a speech-evoked emotion cognitive experiment, with full description in Chen et al ( 10 ). Nineteen healthy participants (8 females and 11 males) with a mean age of 22.4 years (ranging between 18 and 27 years) participated in the experiment.…”

“…A series of computational methods and tools have been developed to deal with such data challenges ( 7 – 9 ). Particularly, a variety of signal analysis methods have been proposed to capture the characteristics of the EEG signals ( 10 , 11 ). Among them, time-frequency analysis methods are found efficient in discovering the complex hidden features underlying EEG signals ( 12 ).…”

Trait and state mindfulness modulate EEG microstates

“…This could indicate the activation of emotion successful integration networks [Britz et al, 2010] specifically in meditators explaining the clustering identification of microstate C. In fact, topographies for meditators were stable across recording periods whereas non-meditators present differences between the three periods. Studies suggest a high sensibility of microstates to emotions [Chen et al, 2021] which are viewed as products at the brain network level [Lindquist et al, 2012]. In particular, the DNM plays a key role in the construction of discrete emotional experiences [Satpute and Lindquist, 2019].…”

Emotion Recognition With Knowledge Graph Based on Electrodermal Activity

“…However, the dependability of the physical-based data can be equivocal as it can be easy for people to intentionally alter their reactions, which results in a false reflection of their real emotions. Physiological-based signals on the other hand include electroencephalogram (EEG), electrocardiogram (ECG), electromyogram (EMG), electrodermal activity (EDA)/galvanic skin response (GSR), blood volume pulse (BVP), temperature, photoplethysmography (PPG), respiration (RSP), and so on (Chen et al, 2021 ). While people may know the reasons why their signals are being collected, physiological signals relating to emotional states are hard to manipulate because they are controlled by the autonomic nervous system (ANS) (Shu et al, 2018 ).…”