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Proceedings Article

Wavelet domain analysis of EEG data for emotion recognition: evaluation of recoursing energy efficiency

[+] Author Affiliations
Theus H. Aspiras, Vijayan K. Asari

Univ. of Dayton (USA)

Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 805818 (June 03, 2011); doi:10.1117/12.884074
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From Conference Volume 8058

  • Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
  • Harold Szu
  • Orlando, Florida, USA | April 25, 2011

abstract

In this paper, we evaluate the feature extraction technique of Recoursing Energy Efficiency on electroencephalograph data for human emotion recognition. A protocol has been established to elicit five distinct emotions (joy, sadness, disgust, fear, surprise, and neutral). EEG signals are collected using a 256-channel system, preprocessed using band-pass filters and Laplacian Montage, and decomposed into five frequency bands using Discrete Wavelet Transform. The Recoursing Energy Efficiency (REE) is calculated and applied to a Multi-Layer Perceptron network for classification. We compare the performance of REE features with conventional energy based features.

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Citation

Theus H. Aspiras and Vijayan K. Asari
"Wavelet domain analysis of EEG data for emotion recognition: evaluation of recoursing energy efficiency", Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 805818 (June 03, 2011); doi:10.1117/12.884074; http://dx.doi.org/10.1117/12.884074


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