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Shahriman Abu Bakar
Preferred name
Shahriman Abu Bakar
Official Name
Shahriman, Abu Bakar
Alternative Name
Bakar, Shahriman A.B.
Shariman, A. B.
Ab, Shahriman
Abu Bakar, S.
Bakar, A. S.
Bakar, S. A.
Bakar, Shahriman Abu
Bakar, Sharifah Adzila Syed Abu
Bakar, S. Abu
Main Affiliation
Scopus Author ID
57196198202
Researcher ID
ELT-0087-2022
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PublicationAn emotion assessment of stroke patients by using bispectrum features of EEG Signals( 2020)
;Choong Wen Yean ;Murugappan Murugappan ;Yuvaraj Rajamanickam ;Mohammad Iqbal Omar ;Bong Siao ZhengEmotion 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.