An out-of-the-lab evaluation of dry EEG technology on a large-scale motor imagery brain-computer interface dataset

<i>Objective</i>. This study assesses the signal quality of state-of-the-art dry electroencephalography (EEG) under highly challenging, uncontrolled, real-world conditions and compares it to conventional wet EEG.<i>Approach</i>. EEG data from 530 participants recorded during a public exhibition were benchmarked against several established signal quality metrics, including spiking activity, kurtosis, auto-mutual information, spectral entropy, gamma-band power, and parameters extracted using the fitting oscillations and one-over F model. Additionally, ICLabel decomposition was applied to quantify artifact influences across EEG channels. Dry electrode results were compared with their equivalents extracted on two control datasets comprising 71 and 80 participants, respectively, recorded with wet EEG systems in laboratory, home, or clinical surroundings.<i>Main results</i>. The analysis revealed condition-specific susceptibility to artifacts for both EEG modalities. The dry EEG system exhibited substantial robustness in moderate-noise scenarios, with artifact profiles comparable to controlled wet EEG recordings. However, recordings obtained in highly dynamic conditions showed increased muscle artifacts and broadband activity, notably in frontal and temporal regions. Wet EEG systems, under controlled conditions, were overall less inflicted by artifacts, yet, fronto-central ocular and muscular artifacts were consistently present. ICLabel analysis further confirmed these findings, indicating similar proportions of brain-related activity across systems (approximately 31%-49.5%), but highlighted increased vulnerability to movement and environmental artifacts in dry EEG during dynamic tasks.<i>Significance</i>. In agreement with recent similar investigations, our findings demonstrate that dry EEG caps have significantly matured, achieving signal quality comparable to wet EEG systems even in challenging real-world conditions, provided appropriate artifact mitigation strategies are employed. These results affirm the practical readiness and broad feasibility of dry EEG technologies for diverse Brain-computer interface applications in naturalistic environments.