Clinical NeuroscienceA statistically robust EEG re-referencing procedure to mitigate reference effect

•A novel procedure is introduced based upon robust statistical estimation to mitigate EEG reference effect.•The new procedure can reduce bias, as is demonstrated in simulation and with human EEG recordings.•Subsequent inter-regional coupling estimates can be improved.•The procedure is simple and fast.

BackgroundThe electroencephalogram (EEG) remains the primary tool for diagnosis of abnormal brain activity in clinical neurology and for in vivo recordings of human neurophysiology in neuroscience research. In EEG data acquisition, voltage is measured at positions on the scalp with respect to a reference electrode. When this reference electrode responds to electrical activity or artifact all electrodes are affected. Successful analysis of EEG data often involves re-referencing procedures that modify the recorded traces and seek to minimize the impact of reference electrode activity upon functions of the original EEG recordings.New methodWe provide a novel, statistically robust procedure that adapts a robust maximum-likelihood type estimator to the problem of reference estimation, reduces the influence of neural activity from the re-referencing operation, and maintains good performance in a wide variety of empirical scenarios.ResultsThe performance of the proposed and existing re-referencing procedures are validated in simulation and with examples of EEG recordings. To facilitate this comparison, channel-to-channel correlations are investigated theoretically and in simulation.Comparison with existing methodsThe proposed procedure avoids using data contaminated by neural signal and remains unbiased in recording scenarios where physical references, the common average reference (CAR) and the reference estimation standardization technique (REST) are not optimal.ConclusionThe proposed procedure is simple, fast, and avoids the potential for substantial bias when analyzing low-density EEG data.