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

Abstract

High-density Electroencephalography (HD-EEG) has proven to be the EEG montage that estimates the neural activity inside the brain with highest accuracy. Multiple studies have reported the effect of electrode number on source localization for specific sources and specific electrode configurations. The electrodes for these configurations are often manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, but electrode configurations are not often selected according to their contribution to estimation accuracy. In this work, an optimization-based study is proposed to determine the minimum number of electrodes that can be used and to identify the 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 datasets with known ground-truth. The experimental results show that optimal subsets with 6 electrodes can attain 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 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 cases respectively. Additionally, for such electrode numbers, lower mean errors and standard deviations than with 231 electrodes were obtained.

Automatic Sleep Stage Classification with Optimized Selection of EEG Channels

“…Here the recordings were done with 128 EEG channels, and the NSGA-II optimized search, the goal is to achieve the highest accuracy/F1 score while at the same time using the minimum number of EEG channels. This approach of minimizing the number of electrodes has proven to be effective to identify subset of channels while retaining high accuracy in multiple problems [28], [29]. Using the NSGA algorithm, the number of channels were constrained around 5,4,3,2,1.…”

“…1) Preprocessing: For the input to this model the data was first decomposed in sub-bands using the Fourier Decomposition Method (FDM) [26]. The following frequency bands were extracted: [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] Hz, [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz, [8][9][10][11][12] Hz, [4][5][6][7][8] Hz, [0,4] Hz, [0.5-2] Hz, [2][3][4][5]…”

Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders

“…EEG is a non-invasive technique for measuring the brain's electrical activity [26][27][28][29]. Recent advances in EEG technology include High-density electrode arrays: EEG systems with high-density electrode arrays, consisting of hundreds of electrodes, have become increasingly common in recent years [30]. These systems allow for more precise localization of brain activity and can better capture the dynamics of neural networks.…”

ECG-Based Driving Fatigue Detection Using Heart Rate Variability Analysis with Mutual Information

“…Another popular fatigue measurement method is measuring fatigue through physiological signals using EEG, for example [15]. This method has adaptability to any driver condition, is suitable for any driving conditions, and generally has high accuracy because it measures brain activity directly; however, it is not practical since it needs at least six electrodes placed on the scalp to measure brain signals [19].…”