ArticleBrain sciences2020
An Emotion Assessment of Stroke Patients by Using Bispectrum Features of EEG Signals.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Editorial for the Special Issue "Medical Data Processing and Analysis-2nd Edition".Diagnostics (Basel, Switzerland) · 2025Article
- Depression assessment using integrated multi-featured EEG bands deep neural network models: Leveraging ensemble learning techniques.Computational and structural biotechnology journal · 2024Article
- FER-PCVT: Facial Expression Recognition with Patch-Convolutional Vision Transformer for Stroke Patients.Brain sciences · 2022Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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