Automated methodology for optimal selection of minimum electrode subsets for accurate EEG source estimation based on Genetic Algorithm optimization

Abstract

ABSTRACTHigh-density Electroencephalography (HD-EEG) has been proven to be the most accurate option to estimate the neural activity inside the brain. Multiple studies report the effect of electrode number on source localization for specific sources and specific electrode configurations. The electrodes for these configurations have been manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, but electrodes were not selected according to their contribution to accuracy. In this work, an optimization-based study is proposed to determine the minimum number of electrodes and identify optimal combinations of electrodes that can retain the localization accuracy of HD-EEG reconstructions. This optimization approach incorporates scalp landmark positions of widely used EEG montages. In this way, a systematic search for the minimum electrode subset is performed for single and multiple source localization problems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with source reconstruction methods is used to formulate a multi-objective optimization problem that concurrently minimizes 1) the localization error for each source and 2) the number of required EEG electrodes. The method can be used for evaluating the source localization quality of low-density EEG systems (e.g. consumer-grade wearable EEG). We performed an evaluation over synthetic and real EEG dataset with known ground-truth. The experimental results show that optimal subsets with 6 electrodes can obtain an equal or better accuracy than HD-EEG (with more than 200 channels) for a single source case. This happened when reconstructing a particular brain activity in more than 88% of the cases (in synthetic signals) and 63% (in real signals), and in more than 88% and 73% of the cases when considering optimal combinations with 8 channels. For a multiple-source case of three sources (only with synthetic signals), it was found that optimized combinations of 8, 12 and 16 electrodes attained an equal or better accuracy than HD-EEG with 231 electrodes in at least 58%, 76%, and 82% of the cases respectively. Additionally, for such electrode numbers, a lower mean error and standard deviation than with 231 electrodes were obtained.HighlightsThe number of EEG electrodes and their locations can be optimized for reconstructing the brain source activity.Optimally selected EEG electrodes can retain the accuracy of high density montages (256, 128 chs) for brain source estimation, when electrodes are selected according to their contribution to accuracy.With optimization, selected combinations of EEG electrodes will flexibilize the estimation of the source activity.