EEG source imaging assists decoding in a face recognition task

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2017

DOI

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EEG based brain state decoding has numerous applications. State of the art decoding is based on processing of the multivariate sensor space signal, however evidence is mounting that EEG source reconstruction can assist decoding. EEG source imaging leads to high-dimensional representations and rather strong a priori information must be invoked. Recent work by Edelman et al. (2016) has demonstrated that introduction of a spatially focal source space representation can improve decoding of motor imagery. In this work we explore the generality of Edelman et al. hypothesis by considering decoding of face recognition. This task concerns the differentiation of brain responses to images of faces and scrambled faces and poses a rather difficult decoding problem at the single trial level. We implement the pipeline using spatially focused features and show that this approach is challenged and source imaging does not lead to an improved decoding. We design a distributed pipeline in which the classifier has access to brain wide features which in turn does lead to a 15% reduction in the error rate using source space features. Hence, our work presents supporting evidence for the hypothesis that source imaging improves decoding.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
Publication date2017
Pages939-943
ISBN (print)9781509041176
DOIs
StatePublished - 2017
Event19th International Conference on Acoustics, Speech and Signal Processing - Kyoto, Japan

Conference

Conference19th International Conference on Acoustics, Speech and Signal Processing
LocationKyoto Brighton Hotel
CountryJapan
CityKyoto
Period05/03/201709/03/2017
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149
CitationsWeb of Science® Times Cited: No match on DOI

    Keywords

  • Pipelines, Electroencephalography, Decoding, Feature extraction, Face, Error analysis, Face recognition, Brain state decoding, Brain computer interface, BCI, EEG source imaging
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