Reference Electrode Standardization Interpolation Technique (RESIT): A Novel Interpolation Method for Scalp EEG

Li Dong,Jianfu Li,Yongxiu Lai,Fali Li,Xue Yu,Tiejun Liu,Lingling Zhao,Yufan Zhang,Dezhong Yao

doi:10.1007/s10548-021-00844-2

Abstract

“Bad channels” are common phenomena during scalp electroencephalography (EEG) recording that arise due to various technique-related reasons, and reconstructing signals from bad channels is an inevitable choice in EEG processing. However, current interpolation methods are all based on purely mathematical interpolation theory, ignoring the neurophysiological basis of the EEG signals, and their performance needs to be further improved, especially when there are many scattered or adjacent bad channels. Therefore, a new interpolation method, named the reference electrode standardization interpolation technique (RESIT), was developed for interpolating scalp EEG channels. Resting-state and event-related EEG datasets were used to investigate the performance of the RESIT. The main results showed that (1) assuming 10% bad channels, RESIT can reconstruct the bad channels well; (2) as the percentage of bad channels increased (from 2% to 85%), the absolute and relative errors between the true and RESIT-reconstructed signals generally increased, and the correlations between the true and RESIT signals decreased; (3) for a range of bad channel percentages (2% ~ 85%), the RESIT had lower absolute error (approximately 2.39% ~ 33.5% reduction), lower relative errors (approximately 1.3% ~ 35.7% reduction) and higher correlations (approximately 2% ~ 690% increase) than traditional interpolation methods, including neighbor interpolation (NI) and spherical spline interpolation (SSI). In addition, the RESIT was integrated into the EEG preprocessing pipeline on the WeBrain cloud platform (https://webrain.uestc.edu.cn/). These results suggest that the RESIT is a promising interpolation method for both separate and simultaneous EEG preprocessing that benefits further EEG analysis, including event-related potential (ERP) analysis, EEG network analysis, and strict group-level statistics.

Highlights

Scalp electroencephalography (EEG) is a commonly used and excellent technique that directly quantifies the electric fields of brain activity with millisecond temporal resolution through a variable number of electrodes placed on the scalp (Cohen 2017)
A visual inspection of the results showed that, assuming 10% bad channels, all methods can reconstruct the bad channels for cases 1 and 2 to some degree
Using one-way repeated ANOVA (p < 0.01) and post hoc paired t-test (p < 0.005), for the entire range of bad channel percentages (2% to 85%), almost all differences in the pairwise comparisons of the errors and R among these interpolation methods were significant

Summary

Introduction

Scalp electroencephalography (EEG) is a commonly used and excellent technique that directly quantifies the electric fields of brain activity with millisecond temporal resolution through a variable number of electrodes placed on the scalp (Cohen 2017). The removal of bad channels may increase the potential risk of errors for large-scale EEG batch processing while using EEG tools (e.g., EEGLAB (Delorme and Makeig 2004) and FieldTrip (Oostenveld et al 2011)) or cloud platforms (e.g., the WeBrain: https://webrain.uestc.edu.cn/). For these reasons, reconstructing the EEG signals of these bad channels is an alternative and inevitable approach to directly removing them

Results

Discussion

Conclusion