Authors:
Andres Soler
1
;
Eduardo Giraldo
2
;
Lars Lundheim
3
and
Marta Molinas
1
Affiliations:
1
Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway
;
2
Department of Electrical Engineering, Universidad Tecnológica de Pereira, Pereira, Colombia
;
3
Department of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway
Keyword(s):
Relevance Channel Selection, Low-density EEG, EEG Signals, Source Reconstruction.
Abstract:
Electroencephalography (EEG) Source Reconstruction is the estimation of the underlying neural activity at cortical areas. Currently, the most accurate estimations are done by combining the information registered by high-density sets of electrodes distributed over the scalp, with realistic head models that encode the morphology and conduction properties of different head tissues. However, the use of high-density EEG can be unpractical due to the large number of electrodes to set up, and it might not be required in all the EEG applications. In this study, we applied relevance criteria for selecting relevant channels to identify low-density subsets of electrodes that can be used to reconstruct the neural activity on given brain areas, while maintaining the reconstruction quality of a high-density system. We compare the performance of the proposed relevance-based selection with multiple high- and low-density montages based on standard montages and coverage during the reconstruction proce
ss of multiple sources and areas. We assessed several source reconstruction algorithms and concluded that the localization accuracy and waveform of reconstructed sources with subsets of 6 and 9 relevant channels can be comparable with reconstructions done with a distributed set of 128 channels, and better than 62 channels distributed in standard 10-10 positions.
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