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Title: An emotion assessment of stroke patients by using bispectrum features of EEG signals
Authors: Yean, Choong Wen
Wan Khairunizam Wan Ahmad
Wan Azani Mustafa
Murugappan, Murugappan
Rajamanickam, Yuvaraj
Abdul Hamid Adom
Mohammad Iqbal Omar
Zheng, Bong Siao
Ahmad Kadri Junoh
Zuradzman Mohamad Razlan
Shahriman Abu Bakar
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Yean, C. W., Wan Khairunizam Wan Ahmad, Wan Azani Mustafa, Murugappan, M., Rajamanickam, Y., Abdul Hamid Adom, . . . Shahriman Abu Bakar. (2020). An emotion assessment of stroke patients by using bispectrum features of EEG signals. Brain Sciences, 10(10), 672-. doi:10.3390/brainsci10100672
Journal: Brain Sciences
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.
ISSN: 2076-3425
DOI: 10.3390/brainsci10100672
Rights: © 2020 The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
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