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• W2891840684 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." @default.
• W2891840684 created "2018-09-27" @default.
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• W2891840684 date "2018-09-18" @default.
• W2891840684 modified "2023-10-13" @default.
• W2891840684 title "Emotion classification using flexible analytic wavelet transform for electroencephalogram signals" @default.
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• W2891840684 doi "https://doi.org/10.1007/s13755-018-0048-y" @default.
• W2891840684 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6143498" @default.
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