Applied Potential: Neuroergonomic Error Detection in Single Electrode Electroencephalography

by | Mar 14, 2022

The present state of the art in fixed, laboratory electroencephalograms (EEGs) are multi-channel devices, which provide superior spatial coverage of functional regions of the brain, as well as rich variance–covariance data to support postprocessing approaches such as independent component analysis (ICA). In contrast, functional design of wearable electroencephalographic (EEG) equipment represents a balancing act in which each additional channel adds weight, artifact-inducing moving parts, computational requirements, and associated power consumption. Brain processes per se are often already well studied, and optimal electrode placement known and described. As such, applied efforts should leverage this knowledge to employ the lowest number of electrodes allowing consistent detection of the pattern of interest. The ideal here would be one. Nevertheless, can single-electrode EEG, devoid of ICA postprocessing, perform well enough in the electrically noisy real world? Here, we build on previous work to address this question. Sawyer et al. described, for the first time, the detection of the error-related negativity (ERN) evoked-response potential (ERP) in a visual search for complex stimuli. In this work, participants completed tasks during eight-channel EEG recording, which was then analyzed using ICA postprocessing. These same data, restricted to the central scalp electrode (Cz), the electrode closest to the focus of the ERN signal, and without ICA postprocessing, was here reanalyzed. Visual inspection and subsequent statistical analyses of these resultant time-locked ERP data clearly demonstrate that the ERN was detectable under both analytic approaches. Further, a large effect size was seen for both analyses, clearly showing that ERN ERP may be robustly detected in aggregate single-electrode encephalography data.

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