TITLE:
Emotion Classification from EEG Signals Using Time-Frequency-DWT Features and ANN
AUTHORS:
Adrian Qi-Xiang Ang, Yi Qi Yeong, Wee Wee
KEYWORDS:
EEG, Emotion Classification, Feature Extraction
JOURNAL NAME:
Journal of Computer and Communications,
Vol.5 No.3,
March
13,
2017
ABSTRACT: This paper proposes the use of time-frequency and wavelet transform features for emotion recognition via EEG signals. The proposed experiment has been carefully designed with EEG electrodes placed at FP1 and FP2 and using images provided by the Affective Picture System (IAP), which was developed by the University of Florida. A total of two time-domain features, two frequen-cy-domain features, as well as discrete wavelet transform coefficients have been studied using Artificial Neural Network (ANN) as the classifier, and the best combination of these features has been determined. Using the data collected, the best detection accuracy achievable by the proposed schemed is about 81.8%.