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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.

© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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