ArticleHealth information science and systems2018
Emotion classification using flexible analytic wavelet transform for electroencephalogram signals.
Article in Health information science and systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Effectiveness assessment of repetitive transcranial alternating current stimulation with concurrent EEG and fNIRS measurement.Health information science and systems · 2023Article
- S-LSTM-ATT: a hybrid deep learning approach with optimized features for emotion recognition in electroencephalogram.Health information science and systems · 2023Article
- PS-VTS: particle swarm with visit table strategy for automated emotion recognition with EEG signals.Health information science and systems · 2023Article
- Guest Editorial: Special issue on "Application of artificial intelligence in health research".Health information science and systems · 2020Article
- A space-frequency localized approach of spatial filtering for motor imagery classification.Health information science and systems · 2020Article
- Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers.Sensors (Basel, Switzerland) · 2020Article
- A performance based feature selection technique for subject independent MI based BCI.Health information science and systems · 2019Article
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Authors and funding
3 authors.
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Abstract
Emotion based brain computer system finds applications for impaired people to communicate with surroundings. In this paper, electroencephalogram (EEG) database of four emotions (happy, fear, sad, and relax) is recorded and flexible analytic wavelet transform (FAWT) is proposed for the emotion classification. FAWT analyzes the EEG signal into sub-bands and statistical measures are computed from the sub-bands for extraction of emotion specific information. The emotion classification performance of sub-band wise extracted features is examined over the variants of k-nearest-neighbor (KNN) classifier. The weighted-KNN provides the best emotion classification performance 86.1% as compared to other KNN variants. The proposed method shows better emotion classification performance as compared to other existing four emotions classification methods.
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