Cazzolli, G

Gu, Y

Miceli, G

Murphy, B

Poesio, M

University of Essex Research Repository

POSTER PRESENTATION Open AccessEEG study of the neural representation andclassification of semantic categories of animals vstools in young and elderly participantsYuqiao Gu1*, Giulia Cazzolli2, Brian Murphy3, Gabriele Miceli2, Massimo Poesio1,4From Twenty Second Annual Computational Neuroscience Meeting: CNS*2013Paris, France. 13-18 July 2013Our semantic knowledge is essential for unimpairedcognition, language and behavior. Previously, we haveused Electroencephalography (EEG) to decode thesemantic categories of animals vs tools in younger adultsubjects during a covert image naming task[1]. In thiswork, we are extending this approach to older subjects.To date we have recorded EEG from 6 normal oldercontrols. A set of EEG features were extracted from thetime-domain at a range of temporal scales. These signalamplitude features were used as inputs to L2-penalisedlogistic regression classifier, after univariate Anova fea-ture selection. Figure 1 compares the classificationresults of the older group (60-79yrs, mean 68) to theprevious cohort of 7 younger participants (25-33yrs,mean 29). The mean accuracy for these two groupsincreases similarly with the number of channels used asinput to the classifier, and reaches a saturation valuearound 75% (Figure 1A), where baseline is 50%, and anaccuracy >56% is significant at a = 0.05. Using a narrowsliding window to extract features (width of 25 ms,moving in steps of 10 ms) over the period 200 msbefore to 1000 ms after stimulus onset, we found thatthe accuracy peaks higher and earlier in the youngersubjects (Figure 1B), but the contrast only approachedsignificance with the current sample size. We also calcu-lated the mean classification accuracy of each channeland plotted them as scalp-maps. The results show thatthe accuracy is higher in right frontal and occipitalregions in younger subjects (Figure 1C), while the accu-racy is higher in occipital, parietal and left frontal areasin older subjects (Figure 1D).ConclusionThe overall mean classification accuracy of younger andolder groups increases similarly with number of chan-nels. Further results indicate that the accuracy of thetwo groups varies differentially with time and spatiallocation. We hypothesize that differences in accuracyover time might reflect changes in neuronal and synap-tic activity, while differences in accuracy with scalp loca-tion indicate that the semantic network might undergoa re-organization process with age increase. To test thishypothesis further recording and analysis are required.Data on elderly controls will serve as reference for clini-cal applications of EEG recordings in the diagnosis andtreatment of language (especially semantic) disorders inneurodegenerative and other diseases of the central ner-vous system.* Correspondence: yuqiao.gu@unitn.it1CLIC, CIMeC - Center for Mind/Brain Sciences, Università degli Studi diTrento, Rovereto (TN), I - 38068, ItalyFull list of author information is available at the end of the articleFigure 1 Comparison of classification accuracy of animals vstools between younger and older groups.Gu et al. BMC Neuroscience 2013, 14(Suppl 1):P318http://www.biomedcentral.com/1471-2202/14/S1/P318© 2013 Gu et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.AcknowledgementsThis work was made possible through funding by the Fondazione CARITRO(ADAM Project), by the Provincia di Trento, and in collaboration with CogitoSRL (Deep Relations project).Author details1CLIC, CIMeC - Center for Mind/Brain Sciences, Università degli Studi diTrento, Rovereto (TN), I - 38068, Italy. 2CeRiN, CIMeC - Center for Mind/BrainSciences, Università degli Studi di Trento, Rovereto (TN), I - 38068, Italy.3School of Computer Science, Carnegie Mellon University, Pittsburgh, PA15213-3891, USA. 4School of Computer Science and Electronic Engineering,University of Essex, Colchester, CO7 9QZ, UK.Published: 8 July 2013Reference1. Murphy B, Poesio M, Bovolo F, Bruzzone L, Dalponte M, Lakany H: EEGdecoding of semantic category reveals distributed representations forsingle concepts. Brain and Language 2011, 117:12-22.doi:10.1186/1471-2202-14-S1-P318Cite this article as: Gu et al.: EEG study of the neural representation andclassification of semantic categories of animals vs tools in young andelderly participants. BMC Neuroscience 2013 14(Suppl 1):P318.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitGu et al. BMC Neuroscience 2013, 14(Suppl 1):P318http://www.biomedcentral.com/1471-2202/14/S1/P318Page 2 of 2

EEG study of the neural representation and classification of semantic categories of animals vs tools in young and elderly participants

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