A multi-channel EEG system featuring single-wire data aggregation via FM-FDM techniques

Warchall, Julian; Balakrishnan, Aishwarya; Balkan, Ozgur; Mercier, Patrick P.; Garudadri, Harinath; Hairston, W. David; Theilmann, Paul

doi:10.1109/iscas.2016.7527293

Cited by 3 publications

(1 citation statement)

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“…EEG amplifiers can have varying parameters like noise performance, power consumption, signal bandwidth, and cost (Badillo et al, 2003; Harrison and Charles, 2003; Lin et al, 2006; Hairston et al, 2015). More recent developments include wireless data transition directly from a cap (De Vos et al, 2014), ultra-low power digitization, and a minimal use of cables (Warchall et al, 2016). All of these aspects have a potential to alter EEG data.…”

Section: Introductionmentioning

confidence: 99%

Systems, Subjects, Sessions: To What Extent Do These Factors Influence EEG Data?

Melnik

Legkov

Izdebski

et al. 2017

Front. Hum. Neurosci.

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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.

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