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ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection

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dc.contributor Universitat de Vic. Escola Politècnica Superior
dc.contributor Universitat de Vic. Grup de Recerca en Tecnologies Digitals
dc.contributor International Conference on Bio-inspired Systems and Signal Proceesing (3a: 2010: València)
dc.contributor BIOSIGNALS 2010
dc.contributor.author Solé-Casals, Jordi
dc.contributor.author Vialatte, François B.
dc.contributor.author Pantel, J.
dc.contributor.author Prvulovic, D.
dc.contributor.author Haenschel, C.
dc.contributor.author Cichocki, Andrej
dc.date.accessioned 2014-04-07T12:06:49Z
dc.date.available 2014-04-07T12:06:49Z
dc.date.issued 2010
dc.identifier.citation Sole-Casals, J., Vialatte, F., Pantel, J., Prvulovic, D., Haenschel, C., & Cichocki, A. (2010). ICA cleaning procedure for EEG signals analysis: Application to Alzheimer's disease detection. , València 485-490. ca_ES
dc.identifier.uri http://hdl.handle.net/10854/2857
dc.description.abstract To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude (􀂕�100 􀈝�V). We then evaluated the outcome of this cleaning by means of the classification of patients using multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure. ca_ES
dc.format application/pdf
dc.format.extent 6 p. ca_ES
dc.language.iso eng ca_ES
dc.rights Tots els drets reservats ca_ES
dc.subject.other Alzheimer, Malaltia d' ca_ES
dc.title ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection ca_ES
dc.type info:eu-repo/semantics/conferenceObject ca_ES
dc.rights.accesRights info:eu-repo/semantics/openAccess ca_ES

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