Relevance-based Channel Selection for EEG Source Reconstruction: An Approach to Identify Low-density Channel Subsets

Abstract

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 (More)