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• W2076040904 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." @default.
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• W2076040904 date "2011-05-13" @default.
• W2076040904 modified "2023-09-25" @default.
• W2076040904 title "Wavelet domain analysis of EEG data for emotion recognition: evaluation of recoursing energy efficiency" @default.
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• W2076040904 doi "https://doi.org/10.1117/12.884074" @default.
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