2015 7th International IEEE/EMBS Conference on Neural Engineering (NER) 2015
DOI: 10.1109/ner.2015.7146774
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Mutual information between inter-hemispheric EEG spectro-temporal patterns: A new feature for automated affect recognition

Abstract: Automated electroencephalography (EEG) based affect recognition has gained a lot of interest recently, with clinical (e.g., in autism), human-computer interaction (e.g., affective brain-computer interfaces), neuromarketing, and even multimedia (e.g., affective video tagging) applications. Typically, conventional EEG features such as spectral power, coherence, and frontal asymmetry have been used to characterize affective states. Recently, cross-frequency coupling measures have also been explored. In this paper… Show more

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Cited by 22 publications
(18 citation statements)
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“…While the former two have been widely studied and shown to be related to perception and memory (e.g., theta-gamma coupling Canolty et al, 2006 ), the latter has received lower attention. A few studies have shown amplitude-amplitude coupling effects on personality and motivation (Schutter and Knyazev, 2012 ) and recently, the authors proposed an inter-hemispheric cross-frequency amplitude coupling metric that correlated with affective states (Clerico et al, 2015 ). Notwithstanding, existing coupling metrics typically overlook temporal dynamics and are based on inter-hemispheric synchrony, thus overlook synchronization of other brain regions.…”
Section: Introductionmentioning
confidence: 99%
“…While the former two have been widely studied and shown to be related to perception and memory (e.g., theta-gamma coupling Canolty et al, 2006 ), the latter has received lower attention. A few studies have shown amplitude-amplitude coupling effects on personality and motivation (Schutter and Knyazev, 2012 ) and recently, the authors proposed an inter-hemispheric cross-frequency amplitude coupling metric that correlated with affective states (Clerico et al, 2015 ). Notwithstanding, existing coupling metrics typically overlook temporal dynamics and are based on inter-hemispheric synchrony, thus overlook synchronization of other brain regions.…”
Section: Introductionmentioning
confidence: 99%
“…For any other purposes, permission must be obtained from the IEEE by emailing pubs-permissions@ieee.org. [35] used only 75% of data for leave-one-out cross-validation and the rest for feature selection. * * ACC: classification accuracy; F1: F1-score several studies to evaluate the perceptual influence of quality changes and artifact appearances.…”
Section: B Qoementioning
confidence: 99%
“…There is little agreement on the number of electrodes to use or features to extract from EEG. EEG features are often used to classify emotional dimensions of arousal [87][88][89][90], valence [88][89][90], and dominance [90,91]. Skin conductance measures the resistance of the skin by passing a negligible current through the body.…”
Section: Physiological Modalitymentioning
confidence: 99%