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dc.contributor.authorKarimi-Rouzbahani, Hamid
dc.contributor.authorWoolgar, Alexandra
dc.date.accessioned2022-03-17T10:04:09Z
dc.date.available2022-03-17T10:04:09Z
dc.date.issued2022
dc.date.submitted2021-11-30
dc.identifier.issn1662-4548
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/335087
dc.description.abstractNeural codes are reflected in complex neural activation patterns. Conventional electroencephalography (EEG) decoding analyses summarize activations by averaging/down-sampling signals within the analysis window. This diminishes informative fine-grained patterns. While previous studies have proposed distinct statistical features capable of capturing variability-dependent neural codes, it has been suggested that the brain could use a combination of encoding protocols not reflected in any one mathematical feature alone. To check, we combined 30 features using state-of-the-art supervised and unsupervised feature selection procedures (n = 17). Across three datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and individual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the individual features, were outperformed across most of the time points by the multiscale feature of Wavelet coefficients. Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the EEG neural codes better than any combination of protocols. Our findings put new constraints on the models of neural information encoding in EEG.
dc.languageen
dc.publisherFrontiers Media SA
dc.subjectNeuroscience
dc.subjectneural encoding
dc.subjectmultivariate pattern decoding
dc.subjectEEG
dc.subjectfeature extraction
dc.subjectfeature selection
dc.titleWhen the Whole Is Less Than the Sum of Its Parts: Maximum Object Category Information and Behavioral Prediction in Multiscale Activation Patterns.
dc.typeArticle
dc.date.updated2022-03-17T10:04:09Z
prism.publicationNameFront Neurosci
prism.volume16
dc.identifier.doi10.17863/CAM.82529
dcterms.dateAccepted2022-01-24
rioxxterms.versionofrecord10.3389/fnins.2022.825746
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidWoolgar, Alexandra [0000-0002-8453-7424]
dc.identifier.eissn1662-453X
cam.issuedOnline2022-03-02


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