Abstract
Lab-based electroencephalography (EEG) techniques have matured over decades of research and can produce high-quality scientific data. It is often assumed that the specific choice of EEG system has limited impact on the data and does not add variance to the results. However, many low cost and mobile EEG systems are now available, and there is some doubt as to the how EEG data vary across these newer systems. We sought to determine how variance across systems compares to variance across subjects or repeated sessions. We tested four EEG systems: two standard research-grade systems, one system designed for mobile use with dry electrodes, and an affordable mobile system with a lower channel count. We recorded four subjects three times with each of the four EEG systems. This setup allowed us to assess the influence of all three factors on the variance of data. Subjects performed a battery of six short standard EEG paradigms based on event-related potentials (ERPs) and steady-state visually evoked potential (SSVEP). Results demonstrated that subjects account for 32% of the variance, systems for 9% of the variance, and repeated sessions for each subject-system combination for 1% of the variance. In most lab-based EEG research, the number of subjects per study typically ranges from 10 to 20, and error of uncertainty in estimates of the mean (like ERP) will improve by the square root of the number of subjects. As a result, the variance due to EEG system (9%) is of the same order of magnitude as variance due to subjects (32%/sqrt(16) = 8%) with a pool of 16 subjects. The two standard research-grade EEG systems had no significantly different means from each other across all paradigms. However, the two other EEG systems demonstrated different mean values from one or both of the two standard research-grade EEG systems in at least half of the paradigms. In addition to providing specific estimates of the variability across EEG systems, subjects, and repeated sessions, we also propose a benchmark to evaluate new mobile EEG systems by means of ERP responses.
Highlights
Electroencephalography (EEG) techniques have matured over the last 80-plus years since the study of the electrical activity of the human brain by Berger (1935)
Processing of auditory stimuli was studied with the N100 event-related potential (ERP) component (Davis, 1939) and the N170 ERP component relates to the neural processing of faces (Rossion and Caharel, 2011)
Paradigm 1: Auditory Evoked Potentials Late-latency auditory evoked potentials (AEPs) ‘‘beginning with P1 at about 80 ms through to N2 at about 250 ms, all are cortical in origin and maximal in amplitude at the central top of the scalp’’ (Kraus and Nicol, 2009)
Summary
Electroencephalography (EEG) techniques have matured over the last 80-plus years since the study of the electrical activity of the human brain by Berger (1935). Recording scalp potentials in fixed temporal relation to events, e.g., stimulus presentation or reports of recognition by button presses, allows for the capture of electrocortical activity related to sensory, motor, or cognitive processes. Such scalp potentials recorded and averaged over many trials, called event-related potential (ERP; Luck, 2005; Sur and Sinha, 2009), help to reveal important insights about how the human brain works. EEG is one of the most widely used techniques in noninvasive brain research to study correlates of perceptual, cognitive, and motor activity associated with processing of information
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