Title
Assessment of emotional states in EEG signals using multi-frequency power spectrum and functional connectivity patterns
Abstract
In this work, an attempt has been made to characterize arousal and valence emotional states using Electroencephalogram (EEG) signals and Phase lag index (PLI) based functional connectivity features. For this, EEG signals are considered from a publicly available DEAP database. Signals are decomposed into four frequency bands, namely theta (θ, 4–7 Hz), alpha (a, 8–12 Hz), beta (ß, 13–30 Hz), and gamma (γ, 30–45 Hz). Two features, namely relative PSD and PLI, are calculated from each band of signals with Welch's periodogram. Four classifiers, namely Random Forest (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and K-Nearest Neighbor (KNN), are employed to discriminate the emotional states. Results show that the proposed approach can differentiate emotional states using EEG signals. It is observed that there is strong functional connectivity in Fp1-02 and Fp2-Pz in all emotional states for different frequency bands. SVM classifier yields the highest classification performance for arousal, and RF yields the highest performance for valence in the y band. The combination of all features performs the best for the valence dimension. Thus, the proposed approach could be extended for classifying various emotional states in clinical settings. Clinical Relevance— This establishes PLI based approach for improved classification (fl = 74.77% for Arousal fl = 74.94 for valence) of emotional states
Year
DOI
Venue
2022
10.1109/EMBC48229.2022.9871510
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Keywords
DocType
Volume
Arousal,Discriminant Analysis,Electroencephalography,Emotions,Support Vector Machine
Conference
2022
ISSN
ISBN
Citations 
2375-7477
978-1-7281-2783-5
0
PageRank 
References 
Authors
0.34
3
4