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

[1]

Aftanas L.I., Lotova N.V., Koshkarov V.I., Pokrovskaja V.L., Popov S.A., Makhnev V.P., Non-linear analysis of emotion EEG: calculation of Kolmogorov entropy and the principal Lyapunov exponent, Neurosci Lett., 226, 1, pp. 13-16, (1997)

[2]

Boostani R., Moradi M.H., A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier, J Neural Eng., 1, 4, (2004)

[3]

Frantzidis C.A., Bratsas C., Papadelis C.L., Konstantinidis E., Pappas C., Bamidis P.D., Toward emotion aware computing: an integrated approach using multichannel neurophysiological recordings and affective visual stimuli, IEEE Trans Informn Technol Biomed., 14, 3, pp. 589-597, (2010)

[4]

Petrantonakis P.C., Hadjileontiadis L.J., Emotion recognition from EEG using higher order crossings, IEEE Trans Inform Technol Biomed., 14, 2, pp. 186-197, (2010)

[5]

Wang X.-W., Nie D., Lu B.-L., EEG-Based Emotion Recognition Using Frequency Domain Features and Support Vector Machines, Neural Information Processing, pp. 734-743, (2011)

[6]

Chanel G., Kierkels J.J., Soleymani M., Pun T., Short-term emotion assessment in a recall paradigm, Int J Hum-Comput Stud., 67, 8, pp. 607-627, (2009)

[7]

Lin Y.P., Wang C.H., Jung T.P., Wu T.L., Jeng S.K., Duann J.R., Chen J.H., EEG-based emotion recognition in music listening, IEEE Trans Biomed Eng, 57, 7, pp. 1798-1806, (2010)

[8]

Hadjidimitriou S.K., Hadjileontiadis L.J., Toward an EEG-based recognition of music liking using time-frequency analysis, IEEE Trans Biomed Eng., 59, 12, pp. 3498-3510, (2012)

[9]

Murugappan M., Nagarajan R., Yaacob S., Combining spatial filtering and wavelet transform for classifying human emotions using EEG signals, J Med Biol Eng., 31, 1, pp. 45-51, (2011)

[10]

Bajaj V., Pachori R.B., Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signals, Brain-Computer Interfaces, pp. 215-240, (2014)

← 1 2 3 4 5 →