Validating the wearable MUSE headset for EEG spectral analysis and Frontal Alpha Asymmetry

EEG power spectral density (PSD), the individual alpha frequency (IAF) and the frontal alpha asymmetry (FAA) are all EEG spectral measures that have been widely used to evaluate cognitive and attentional processes in experimental and clinical settings, and that can be used for real-world applications (e.g., remote EEG monitoring, brain-computer interfaces, neurofeedback, neuromodulation, etc.). Potential applications remain limited by the high cost, low mobility, and long preparation times associated with high-density EEG recording systems. Low-density wearable systems address these issues and can increase access to larger and diversified samples. The present study tested whether a low-cost, 4-channel wearable EEG system (the MUSE) could be used to quickly measure continuous EEG data, yielding similar frequency components compared to a research-grade EEG system (the 64-channel BIOSEMI Active Two). MUSE data can be live-streamed using the Lab Stream Layer (LSL), and can therefore be implemented into real-world EEG monitoring, brain-computer interfaces (BCI), or neurofeedback applications. We compare the spectral measures from MUSE EEG data referenced to mastoids to those from BIOSEMI EEG data with two different references for validation (mastoids and average reference). A minimal amount of data was deliberately collected to test the feasibility for real-world applications (EEG setup and data collection being completed in under 5 min). We show that the MUSE can be used to examine power spectral density (PSD) in all frequency bands, the individual alpha frequency (IAF), and frontal alpha asymmetry (FAA). Furthermore, we observed satisfying internal consistency reliability in alpha power and asymmetry measures recorded with the MUSE. However, estimating asymmetry on the IAF did not yield significant advantages relative to the traditional method (average over the 8-13 Hz range). These findings should advance human neurophysiological monitoring using easily accessible wearable neurotechnologies in large samples and increase the feasibility of their implementation in real-world settings.