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Attempted Prediction of Emotional Valence from EEG Using Multidimensional Directed Information

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dc.contributor.advisor Peixoto, Nathalia Clayton A Baker 2022-05-16T16:09:00Z 2022-05-16T16:09:00Z 2022-05
dc.description.abstract Quantitative measurement of a person’s emotional state can aid performance in a number of areas, such as human-machine interactions, and psychological research. Electroencephalogram (EEG) data has shown potential as a predictor of emotional valence based on asymmetric activation patterns between the left and right hemispheres of the prefrontal cortex. Multidimensional directed information (MDI) is a computational tool that allows the measurement of information content transferred between different signals in a connected system, and has previously seen applications in EEG-based affective measurement in order to detect the presence of an emotional response. This study aimed to use MDI with EEG data from published datasets in order to derive a directional bias metric as a predictor for emotional valence based on frontal hemisphere asymmetry. Two methods of MDI computation were attempted; significant differences were observed in results between the two, suggesting possible errors in implementation. Neither method yielded output correlating with valence. en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri *
dc.subject Electroencephalography en_US
dc.title Attempted Prediction of Emotional Valence from EEG Using Multidimensional Directed Information en_US
dc.type Article en_US

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Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

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