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Emotion classification using flexible analytic wavelet transform for electroencephalogram signals

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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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Acknowledgement

Support obtained from the PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur, project titled Brain computer interface for classification of human Emotion, Project No. PDPM IIITDMJ/Dir.Office/officeorder/2016/10-2902 is greatly acknowledged.

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Authors and Affiliations

  1. PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, 452005, India

    Varun Bajaj & Sachin Taran

  2. Electrical and Electronics Engineering Department, Technology Faculty, Firat University, Elazig, Turkey

    Abdulkadir Sengur

Authors
  1. Varun Bajaj
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  2. Sachin Taran
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  3. Abdulkadir Sengur
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Correspondence to Varun Bajaj.

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Bajaj, V., Taran, S. & Sengur, A. Emotion classification using flexible analytic wavelet transform for electroencephalogram signals. Health Inf Sci Syst 6, 12 (2018). https://doi.org/10.1007/s13755-018-0048-y

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