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Reconstruction of EEG from limited channel acquisition using estimated signal correlation

Ramakrishnan, AG and Satyanarayana, JV (2016) Reconstruction of EEG from limited channel acquisition using estimated signal correlation. In: BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 27 . pp. 164-173.

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Official URL: http://dx.doi.org/10.1016/j.bspc.2016.02.004

Abstract

Nearby scalp channels in multi-channel EEG data exhibit high correlation. A question that naturally arises is whether it is required to record signals from all the electrodes in a group of closely spaced electrodes in a typical measurement setup. One could save on the number of channels that are recorded, if it were possible to reconstruct the omitted channels to the accuracy needed for identifying the relevant information (say, spectral content in the signal), required to carry out a preliminary diagnosis. We address this problem from a compressed sensing perspective and propose a measurement and reconstruction scheme. Working with publicly available EEG database, we have demonstrated that up to 12 channels in the 10-10 system of electrode placement can be estimated within an average error of 2% from recordings of the remaining channels. As a limiting case, all the channels of the 10-10 system can be estimated using recordings on the sparser 10-20 system within an error of less than 20% in each of the significant bands: delta, theta, beta and alpha. (C) 2016 Elsevier Ltd. All rights reserved.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
Keywords: Correlated signals; Karhunen-Loeve Transform; Electroencephalography; Motor-imagery tasks; Compressed sensing; Convex optimization; EEG electrode placement
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Depositing User: Id for Latest eprints
Date Deposited: 23 May 2016 07:25
Last Modified: 23 May 2016 07:25
URI: http://eprints.iisc.ac.in/id/eprint/53858

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