Evaluating the Orbit Wearable Headband for Ecologically Valid Mental State Assessment: EEG/PPG Signal Quality and Efficacy in Multimodal Data Analysis

Consumer wearables for mental state assessment face scepticism regarding data quality. This study evaluated whether a consumer-grade EEG/PPG headband (Orbit) can reliably capture mental states in real-world settings. We compared Orbit's EEG (AF7, AF8) and PPG-derived heart rate variability (HRV) signals with a laboratory-standard EEG/ECG device across 31 resting sessions (15 adults), assessing univariate correlations across 18 EEG, 12 HRV features. Participants then completed four cognitive and affective tasks while wearing Orbit, providing subjective feedback. Novel multimodal features, combining short-window individual metrics with long-window complexity from multi-feature cluster sequences, were utilised in machine learning (Random Forest, Gradient Boosting) to classify tasks and predict changes in subjective relaxation and focus. Orbit showed moderate-to-strong correlations with lab measurements for numerous EEG (eyes-closed: 12; eyes-open: 14) and HRV (eyes-closed: 9; eyes-open: 6) features in >50% of recordings. The advanced multimodal features significantly classified all tasks (Random Forest: z=13.08-13.11; Gradient Boosting: z=13.00-13.16, all p<0.001) and predicted combined relaxation/focus ratings (Random Forest: z=2.37; Gradient Boosting: z=1.79, all p<0.05) versus chance. These findings suggest that consumer wearables, employing robust multimodal feature engineering, can effectively capture psychophysiological changes and track mental states, such as focus and relaxation, in naturalistic environments, offering potential for real-world neuroscience applications. Limitations include a modest sample size and restricted demographic diversity, which warrant further research.