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In the past decade, wavelet transforms, Shapelet transforms, Fourier transforms and other efficient signal processing techniques are coupled with machine learning and deep learning for the efficient classification of emotion from EEG signals.
In this work, an efficient model EVSAE is proposed and a best result with an arousal of 96.87%, Valence of 95.45% and Dominance of 96.09% is obtained for EMD+VMD+ Sample Entropy with ALO-ELM classifier for DREAMER dataset.
Also, for the DEAP dataset, the same strategy produces a good result with an arousal of 97.02%, Valence of 95.78% and Dominance of 96.66%.
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IEEE/ICACT20260121 Slide.15
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