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Recognition of Emotional States Using Multiscale Information Analysis of High Frequency EEG Oscillations

1,†, 1,2,*,†, 1 and 1,*
1
Key Laboratory of Child Development and Learning Science, Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210000, China
2
Institute of Biomedical Devices (Suzhou), Southeast University, Suzhou 215000, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Entropy 2019, 21(6), 609; https://doi.org/10.3390/e21060609
Received: 22 May 2019 / Revised: 18 June 2019 / Accepted: 18 June 2019 / Published: 20 June 2019
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Abstract

Exploring the manifestation of emotion in electroencephalogram (EEG) signals is helpful for improving the accuracy of emotion recognition. This paper introduced the novel features based on the multiscale information analysis (MIA) of EEG signals for distinguishing emotional states in four dimensions based on Russell’s circumplex model. The algorithms were applied to extract features on the DEAP database, which included multiscale EEG complexity index in the time domain, and ensemble empirical mode decomposition enhanced energy and fuzzy entropy in the frequency domain. The support vector machine and cross validation method were applied to assess classification accuracy. The classification performance of MIA methods (accuracy = 62.01%, precision = 62.03%, recall/sensitivity = 60.51%, and specificity = 82.80%) was much higher than classical methods (accuracy = 43.98%, precision = 43.81%, recall/sensitivity = 41.86%, and specificity = 70.50%), which extracted features contain similar energy based on a discrete wavelet transform, fractal dimension, and sample entropy. In this study, we found that emotion recognition is more associated with high frequency oscillations (51–100Hz) of EEG signals rather than low frequency oscillations (0.3–49Hz), and the significance of the frontal and temporal regions are higher than other regions. Such information has predictive power and may provide more insights into analyzing the multiscale information of high frequency oscillations in EEG signals. View Full-Text
Keywords: emotion recognition; EEG; multiscale information analysis; multiscale sample entropy; ensemble empirical mode decomposition; fuzzy entropy; support vector machine emotion recognition; EEG; multiscale information analysis; multiscale sample entropy; ensemble empirical mode decomposition; fuzzy entropy; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Gao, Z.; Cui, X.; Wan, W.; Gu, Z. Recognition of Emotional States Using Multiscale Information Analysis of High Frequency EEG Oscillations. Entropy 2019, 21, 609.

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