Open AccessEmotion classification using EEG signals based on tunable‐ Q wavelet transform
Author(s) -
Krishna Anala Hari,
Sri Aravapalli Bhavya,
Priyanka Kurakula Yuva Venkata Sai,
Taran Sachin,
Bajaj Varun
Publication year - 2019
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2018.5237
Subject(s) - electroencephalography , computer science , artificial intelligence , emotion classification , wavelet transform , classifier (uml) , pattern recognition (psychology) , speech recognition , brain–computer interface , support vector machine , affective computing , emotion recognition , signal (programming language) , wavelet , psychology , neuroscience , programming language
Emotion is a most instinctive feeling of a human. Emotion classification finds application in brain–computer interface systems for the assistance of disabled persons. To recognise the emotional state, electroencephalogram (EEG) signal plays a vital role because it provides immediate response to every state of change in the human brain. Here, the utility of tunable‐ Q wavelet transform (TQWT) is explored for the classification of different emotions EEG signals. TQWT decomposes EEG signal into subbands and time‐domain features are extracted from subbands. The extracted features are used as an input to extreme learning machine classifier for the classification of happy, fear, sad, and relax emotions. Experimental results of the proposed method show better four emotions classification performance when compared with the other existing methods.