Emotion classification using flexible analytic wavelet transform for electroencephalogram signals

被引：34

作者：

Bajaj V. ^{[1
]}

Taran S. ^{[1
]}

Sengur A. ^{[2
]}

机构：

[1] PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur

[2] Electrical and Electronics Engineering Department, Technology Faculty, Firat University, Elazig

来源：

Health Information Science and Systems | / 6卷 / 1期

关键词：

Electroencephalogram; Emotion classification; Flexible analytic wavelet transform; k-nearest-neighbor;

D O I：

10.1007/s13755-018-0048-y

中图分类号：

学科分类号：

摘要：

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. © 2018, Springer Nature Switzerland AG.

引用

共 41 条

[11]

Bajaj V., Pachori R.B., Human emotion classification from EEG signals using multiwavelet transform, In: Medical Biometrics, 2014 International Conference On, IEEE, pp. 125-130, (2014)

[12]

Murugappan M., Human emotion classification using wavelet transform and KNN, Pattern Analysis and Intelligent Robotics (ICPAIR), 2011 International Conference On, 1, pp. 148-153, (2011)

[13]

Petrantonakis P.C., Hadjileontiadis L.J., Emotion recognition from brain signals using hybrid adaptive filtering and higher order crossings analysis, IEEE Trans Affect Comput., 1, 2, pp. 81-97, (2010)

[14]

Liu Y., Sourina O., Nguyen M.K., Real-time EEG-based human emotion recognition and visualization, Cyberworlds (CW), 2010 International Conference On, pp. 262-269, (2010)

[15]

Ang A.Q.X., Yeong Y.Q., Ser W., Emotion classification from EEG signals using time-frequency-DWT features and ANN, J Comput Commun., 5, 3, (2017)

[16]

Singh M., Singh M., Goyal M., Emotion classification using EEG entropy, Int J Inform Technol Knowl Manag., 8, 2, pp. 150-158, (2015)

[17]

Mikhail M., El-Ayat K., Coan J.A., Allen J.J., Using minimal number of electrodes for emotion detection using brain signals produced from a new elicitation technique, Int J Auton Adapt Commun Syst., 6, 1, pp. 80-97, (2013)

[18]

Khosrowabadi R., Quek H.C., Wahab A., Ang K.K., EEG-based emotion recognition using self-organizing map for boundary detection, Pattern Recognition (ICPR), 2010 20Th International Conference On, pp. 4242-4245, (2010)

[19]

Bhatti A.M., Majid M., Anwar S.M., Khan B., Human emotion recognition and analysis in response to audio music using brain signals, Comput Hum Behav., 65, pp. 267-275, (2016)

[20]

Mehmood R.M., Lee H.J., A novel feature extraction method based on late positive potential for emotion recognition in human brain signal patterns, Comput Electr Eng., 53, pp. 444-457, (2016)

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