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Discriminating Multiple Emotional States from EEG Using a Data-Adaptive, Multiscale Information-Theoretic Approach
被引:38
作者:
Tonoyan, Yelena
[1
]
Looney, David
[2
]
Mandic, Danilo P.
[2
]
Van Hulle, Marc M.
[1
]
机构:
[1] Lab Neuro & Psychophysiol, Res Grp Neurophysiol, O&N 2 Herestr 49 Box 1021, B-3000 Leuven, Belgium
[2] Univ London Imperial Coll Sci Technol & Med, Dept Elect & Elect Engn, Commun & Signal Proc Res Grp, Room 813,Level 8,Exhibit Rd, London SW7 2BT, England
基金:
英国工程与自然科学研究理事会;
关键词:
Emotion;
complexity;
multiscale sample entropy;
EMD;
FUZZY SYNCHRONIZATION LIKELIHOOD;
PHYSIOLOGICAL TIME-SERIES;
CEREBRAL ASYMMETRY;
HUMAN BRAIN;
COMPLEXITY;
DIAGNOSIS;
SPECTRUM;
ENTROPY;
THETA;
BAND;
D O I:
10.1142/S0129065716500052
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
A multivariate sample entropy metric of signal complexity is applied to EEG data recorded when subjects were viewing four 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 (MEMD) technique and show that in this way we can discriminate between five 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.
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页数:15
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