Neurodegenerative disorders represent huge global unmet medical needs and require the development of new disease modifying therapies. Given the insidious nature of these disorders and the high cost of many diagnostic tests, there is a significant need for widely available, reliable, and inexpensive biomarkers to track progression of neurodegenerative processes in time frames suitable for drug development. In this context, EEG may have remarkable potential. Although EEG is susceptible to known lifestyle factors and medications, it has many considerable advantages. EEG reflects synaptic activity, which is a common denominator for the functional impact of neurodegenerative processes. EEG is a non-invasive, portable, safe, and inexpensive technology that is widely accepted and requires relatively short acquisition time. Qualitative EEG is routinely utilized in clinical practice for the diagnosis of epilepsy. More recently, an integration of a quantitative EEG biomarker (qEEG) and clinician's evaluation have been proposed for the assessment of attention-deficit/hyperactivity disorder (ADHD) and has been granted from the FDA (Food and Drug Administration) class II designation to support the clinical evaluation of ADHD (Lenartowicz and Loo, 2014; Snyder et al., 2015). qEEG is in investigational stages for use as an endpoint in neurodegenerative diseases in clinical trials. However, recent advances in data analyses, interpretation and improved spatial resolution have increased the potential of EEG as a reliable, accurate biomarker for neurodegenerative disease progression. Many reported observational resting state qEEG analyses support its potential value as a biomarker for detection of neural signatures of neurodegeneration occurring in Alzheimer's disease (Babiloni et al., 2011; Moretti et al., 2011; Berka et al., 2014; Chen et al., 2015; Garn et al., 2015; Ruffini et al., 2016; Waninger et al., 2016), Parkinson's disease (Sarnthein and Jeanmonod, 2007; Babiloni et al., 2011; Soria-Frisch et al., 2014; Shani Waninger et al., 2015; Kroupi et al., 2017) and frontotemporal dementia (Pijnenburg et al., 2008; Nishida et al., 2011; Caso et al., 2012; Goossens et al., 2016).

Over the years, EEG hardware technology has also evolved and several wireless multi-channel systems have emerged that deliver high quality EEG and physiological signals in a simpler, more convenient and comfortable design than the traditional, cumbersome systems. Traditional EEG systems require lengthy assembly and application time, typically involving abrasion of the patient's scalp. The application time and discomfort render these traditional systems challenging to use in populations affected by dementia, where cooperation with lengthy clinical procedures is often difficult. However, several currently available wireless systems can be applied in 20 min or less with no discomfort during application and with a comfortable fit during acquisitions. Combined with advances in signal detection and quantitative analysis techniques, wireless systems are ideal candidates for relatively rapid, tolerable clinical assessment of potentially challenging dementia populations, such as behavioral variant frontotemporal dementia, characterized by prominent behavioral and personality changes.

More recently, there has also been a growing market for consumer wearable technologies leading to limited-channel systems available for personal use, such as meditation and relaxation training. It is conceivable that these systems, albeit their limited coverage, may also be used in selected clinical studies. Application of these consumer systems in clinical trials research has not been extensively explored however and the accuracy and reliability of these systems for repeated measurements have not been well-established. Further, it is not clear whether the limited-channel acquisition may provide sufficient data and anatomical coverage to assess the neural signatures in patients affected by neurodegenerative diseases.

The current study was designed to provide initial evaluation of the potential of consumer EEG systems for clinical trials, by comparing the ease of use, accuracy and reliability of two medical grade, multi-channel wireless EEG systems, B-Alert X24, and Enobio 20, with two consumer, limited-channel systems, Muse and Mindwave.

This study compared quantitative EEG signal and test-retest reliability of medical and consumer EEG systems in order to evaluate their potential application in clinical research and clinical trials. Newly popularized consumer EEG systems were evaluated due to their low cost, wide accessibility, and potential for home based studies in challenging populations. Among the popular applications of consumer EEG systems are meditation and relaxation training as well as coping with anxiety or pain. Recent investigations have been exploring their utility beyond gaming: NeuroSky was shown to be able to detect onset of stage 1 sleep (Van Hal et al., 2014) and there has also been interest in assessing consumer EEG within brain computer-interfaces (Bialas and Milanowski, 2014; Kim et al., 2015; Taherian et al., 2017). The application of simpler EEG systems (6-channels, for example) is also being explored for emergency settings (Jakab et al., 2014). A prior evaluation, although utilizing a more complex headset, showed that commercially available multi-lead consumer EEG systems, such as the Emotiv EPOC 16-electrode cap, may also have value in evaluating clinical conditions (Schiff et al., 2016).

This study provided evidence that fairly good quality EEG data can be successfully collected from consumer EEGs. However, there were distinctions in the power increase, test retest, and shape of the alpha peak observed at 8–13 Hz. Mindwave showed overall similar Fp1 power spectra to the medical systems with a slight broadband increase over B-Alert and Enobio. Muse showed a broadband increase in power spectra, which may reflect artifact in the data acquired by a dry electrode.

Consumer EEG systems did show a significantly more convenient and faster set up,which is optimal for their intended use in entertainment and self-help applications. However, their data quality was overall negatively affected by artifact susceptibility associated with the dry electrode. As expected, the data quality was particularly diminished during EO. The lack of impedance testing capability and application to the frontal region, which is particularly prone to eye blinks and muscle movement with eye opening also likely contributed to this relative artifact. Additionally, the assessment performed by consumer EEG systems is, by their nature, limited and confined to the only anatomical brain region covered by the few channels, precluding multi-networks evaluations.

Dry electrodes may also be more prone to result in discomfort over time and pose a higher risk of misplacement on the forehead, leading to inaccurate signal acquisition and test/retest. Compared to medical grade equipment, test/retest reliability was lower in consumer EEG systems. Reliability was measured with a test-retest acquisition for all systems (Figure 5). While B-Alert, Enobio, and Mindwave performed reasonably well, Muse had relatively low reliability. Further, while a clear alpha peak was demonstrated for Muse on Visit 2 (Figure 4), the absence of an alpha peak on Visit 1 suggests a lack of consistency that may be due to artifact. Consistent reliable measures of brain activity are crucial in clinical trials when monitoring disease progression and evaluating efficacy of an experimental therapeutic. While the consumer systems may be useful for a quick assessment when time is limited, these limitations of consumer EEG could hinder their applications in research and clinical trials settings and a medical grade system with high test-retest reliability is recommended for use as a pharmacodynamic endpoint in clinical assessments.

Ultimately, the comparison of medical and consumer EEG systems under experimental conditions highlighted differentiation in performance and, particularly, specific limitations of use that could hinder the applications of consumer systems in research and clinical trials settings. On the other hand, the medical multi-lead systems are less ideal for entertainment purposes that require rapid setup and data processing from a minimal number of electrodes. The main limitation of this study relies in the small size of the study population and lack of randomization of order of systems used for acquisition. Data was acquired in the same order of systems in order to avoid effects of time of acquisition that can impact EEG due to circadian rhythms. However, EEG power metrics have proven to be a very reliable and repeatable for individuals when using standard EEG systems. For this reason, the selected sample size was considered informative.

In conclusion, EEG data can be successfully collected from all four systems tested, including consumer EEG systems, with varying limitations on usability, data quality and reliability that guide their optimal applications including in clinical trial settings. Susceptibility to artifact and variability in test/retest reliability associated with current consumer EEG systems suggest the use of medical grade EEG system for robust clinical cross-sectional and longitudinal EEG data collections.

ER and AV: participated in the conception and the design of the project, the analysis, and interpretation of data and manuscript preparation. SW, CB, and GR: participated in the conception and the design of the project, its execution, the analysis and interpretation of data and manuscript preparation. All authors have been involved in drafting, writing, and revising the manuscript and they all have read and approved the final version of the manuscript.