Emotion Classification from EEG Signals Using Time-Frequency-DWT Features and ANN - Journal of Computer and Communications

JCC > Vol.5 No.3, March 2017

Emotion Classification from EEG Signals Using Time-Frequency-DWT Features and ANN ()

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Download as PDF (Size: 282KB) PP. 75-79

DOI: 10.4236/jcc.2017.53009 1,821 Downloads 4,063 Views Citations

Author(s)

Adrian Qi-Xiang Ang, Yi Qi Yeong, Wee Wee

Affiliation(s)

School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore City, Singapore.

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%.

KEYWORDS

EEG, Emotion Classification, Feature Extraction

Share and Cite:

Ang, A. , Yeong, Y. and Wee, W. (2017) Emotion Classification from EEG Signals Using Time-Frequency-DWT Features and ANN. Journal of Computer and Communications, 5, 75-79. doi: 10.4236/jcc.2017.53009.

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