Discriminating multiple emotional states from EEG using a data-adaptive, multiscale information-theoretic approach
| Autor: | David Looney, Yelena Tonoyan, Marc M. Van Hulle, Danilo P. Mandic |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Male
Technology Multivariate statistics Multivariate analysis Computer science Speech recognition Entropy Emotions 0801 Artificial Intelligence And Image Processing Electroencephalography Computer Science Artificial Intelligence 0302 clinical medicine EMD FREQUENCY BANDS Artificial Intelligence & Image Processing CLIPS computer.programming_language medicine.diagnostic_test CEREBRAL ASYMMETRY 05 social sciences multiscale sample entropy General Medicine Middle Aged HUMAN BRAIN Female THETA Adult Computer Networks and Communications DIAGNOSIS 050105 experimental psychology Arousal 03 medical and health sciences TIME-SERIES ANALYSIS medicine Entropy (information theory) Humans 0501 psychology and cognitive sciences Time series SPECTRUM DISORDER Aged Emotion Science & Technology COMPLEXITY EVENT-RELATED SYNCHRONIZATION FUZZY SYNCHRONIZATION LIKELIHOOD 1702 Cognitive Science Complexity Sample entropy Computer Science Multivariate Analysis computer 030217 neurology & neurosurgery Photic Stimulation Multiscale sample entropy |
| Popis: | A multivariate sample entropy metric of signal complexity is applied to EEG data recorded when subjects were viewing 4 prior-labeled emotion-inducing video clips from a publically available, validated database. Besides emotion category labels, the video clips also came with arousal scores. Our subjects were also asked to provide their own emotion labels. In total 30 subjects with age range 19–70 years participated in our study. Rather than relying on predefined frequency bands, we estimate multivariate sample entropy over multiple data-driven scales using the multivariate empirical mode decomposition technique (MEMD) and show that in this way we can discriminate between 5 self-reported emotions (p < 0.05). These results could not be obtained by analyzing the relation between arousal scores and video clips, signal complexity and arousal scores, and self-reported emotions and traditional power spectral densities and their hemispheric asymmetries in the theta, alpha, beta, and gamma frequency bands. This shows that multivariate, multiscale sample entropy is a promising technique to discriminate multiple emotional states from EEG recordings. ispartof: International Journal of Neural Systems vol:26 issue:2 ispartof: location:Singapore status: published |
| Databáze: | OpenAIRE |
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