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Nearby scalp channels in multi-channel EEG data exhibit high correlation. Aquestion that naturally arises is whether it is required to record signals fromall the electrodes in a group of closely spaced electrodes in a typicalmeasurement setup. One could save on the number of channels that are recorded,if it were possible to reconstruct the omitted channels to the accuracy neededfor identifying the relevant information (say, spectral content in the signal),required to carry out a preliminary diagnosis. We address this problem from acompressed sensing perspective and propose a measurement and reconstructionscheme. Working with publicly available EEG database, we put our scheme toexperiment and illustrate that if it is only a matter of estimating thefrequency content of the signal in various EEG bands, then all the channelsneed not be recorded. We have achieved an average error below 15% between theoriginal and reconstructed signals with respect to estimation of the spectralcontent in the delta, theta and alpha bands. We have demonstrated that channelsin the 10-10 system of electrode placement can be estimated, with an error lessthan 10% using recordings on the sparser 10-20 system.

Compressed EEG Acquisition with Limited Channels using Estimated Signal Correlation