Identifying electrode bridging from electrical distance distributions: a survey of publicly-available EEG data using a new method.


OBJECTIVE EEG topographies may be distorted by electrode bridges, typically caused by electrolyte spreading between adjacent electrodes. We therefore sought to determine the prevalence of electrode bridging and its potential impact on the EEG literature. METHODS Five publicly-available EEG datasets were evaluated for evidence of bridging using a new screening method that employs the temporal variance of pairwise difference waveforms (electrical distance). Distinctive characteristics of electrical distance frequency distributions were used to develop an algorithm to identify electrode bridges in datasets with different montages (22-64 channels) and noise properties. RESULTS The extent of bridging varied substantially across datasets: 54% of EEG recording sessions contained an electrode bridge, and the mean percentage of bridged electrodes in a montage was as high as 18% in one of the datasets. Furthermore, over 40% of the recording channels were bridged in 9 of 203 sessions. These findings were independently validated by visual inspection. CONCLUSIONS The new algorithm conveniently, efficiently, and reliably identified electrode bridges across different datasets and recording conditions. Electrode bridging may constitute a substantial problem for some datasets. SIGNIFICANCE Given the extent of the electrode bridging across datasets, this problem may be more widespread than commonly thought. However, when used as an automatic screening routine, the new algorithm will prevent pitfalls stemming from unrecognized electrode bridges.

DOI: 10.1016/j.clinph.2013.08.024

Extracted Key Phrases

8 Figures and Tables

Citations per Year

141 Citations

Semantic Scholar estimates that this publication has 141 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Alschuler2014IdentifyingEB, title={Identifying electrode bridging from electrical distance distributions: a survey of publicly-available EEG data using a new method.}, author={Daniel M. Alschuler and Craig E. Tenke and Gerard E. Bruder and J{\"{u}rgen Kayser}, journal={Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, year={2014}, volume={125 3}, pages={484-90} }