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Decoding Emotional Valence from Electroencephalographic Rhythmic Activity

Celikkanat, H; Moriya, H; Ogawa, T; Kauppi, J-P; Kawanabe, M; Hyvarinen, AJ; (2017) Decoding Emotional Valence from Electroencephalographic Rhythmic Activity. In: Park, KS and Kim, Y and Weiland, J, (eds.) 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - 2017. (pp. pp. 4143-4146). IEEE Press: Jeju Island, Korea. Green open access

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Abstract

We attempt to decode emotional valence from electroencephalographic rhythmic activity in a naturalistic setting. We employ a data-driven method developed in a previous study, Spectral Linear Discriminant Analysis, to discover the relationships between the classification task and independent neuronal sources, optimally utilizing multiple frequency bands. A detailed investigation of the classifier provides insight into the neuronal sources related with emotional valence, and the individual differences of the subjects in processing emotions. Our findings show: (1) sources whose locations are similar across subjects are consistently involved in emotional responses, with the involvement of parietal sources being especially significant, and (2) even though the locations of the involved neuronal sources are consistent, subjects can display highly varying degrees of valence-related EEG activity in the sources.

Type: Proceedings paper
Title: Decoding Emotional Valence from Electroencephalographic Rhythmic Activity
Event: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - 2017
Location: Jeju Island, Korea
Dates: 11 July 2017 - 15 July 2017
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/EMBC.2017.8037768
Publisher version: http://doi.org/10.1109/EMBC.2017.8037768
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Integrated circuits, Decoding, Electroencephalography, Motion pictures, Linear discriminant analysis, Feature extraction, Logistics
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: http://discovery.ucl.ac.uk/id/eprint/1557752
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