Publication:
An emotion assessment of stroke patients by using bispectrum features of EEG Signals
An emotion assessment of stroke patients by using bispectrum features of EEG Signals
Date
2020
Authors
Choong Wen Yean
Wan Khairunizam Wan Ahmad
Wan Azani Wan Mustafa
Murugappan Murugappan
Yuvaraj Rajamanickam
Abdul Hamid Adom
Mohammad Iqbal Omar
Bong Siao Zheng
Ahmad Kadri Junoh
Zuradzman Mohamad Razlan
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Abstract
Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8–13) Hz, beta (13–30) Hz and gamma (30–49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.
Description
Keywords
Emotion,
Stroke,
Electroencephalogram (EEG),
Bispectrum