Objective. Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection. Approach. In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording. Main results. The results, presented as area under the receiver operating characteristic curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials. Significance. Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.

Objective. Ocular artefacts from blinks and eye movements remain a persistent obstacle to reliable electroencephalography (EEG), particularly when dedicated electrooculography (EOG) electrodes are unavailable or undesirable. We investigate whether monocular periocular video can provide a vertical EOG (VEOG)-like surrogate for ocular artefact suppression in EEG. Approach. We present VEOS, a video-to-VEOG inference pipeline based on monocular periocular tracking, canthus-defined geometric normalisation, engineered eyelid and iris features, and temporal modelling with a temporal convolutional network (TCN). The inferred VEOG is used as an auxiliary reference in blink-window lagged ridge-regression subtraction of ocular artefacts from EEG. Evaluation used leave-one-subject-out validation on two multimodal datasets: an in-house development dataset (VEOS-Dev; 5 participants with synchronised EEG, EOG and 120 Hz video) and the public Eye–BCI dataset (31 subjects, 63 sessions; high-speed video, EEG and ocular measurements). For centred-window models, a ±ℓ boundary margin prevented temporal leakage between training, validation and test data. Main results. On VEOS-Dev, VEOS achieves median Pearson correlation r = 0.81 to ground-truth VEOG and median blink-onset timing error of 18 ms. On Eye–BCI, the video-only model achieves median r = 0.74, outperforming an eye-tracker baseline. For blink-window cleaning, VEOS suppresses approximately 50% of blink peak-to-peak amplitude on frontal EEG channels, compared with 66% for true VEOG. Cleaning preserves task-relevant EEG structure, including motor imagery mu/beta rhythms, SSVEP spectral signal-to-noise ratio, and P300 morphology. At the participant level, VEOS yields a modest improvement in motor imagery, leaves SSVEP unchanged, and approaches true-VEOG cleaning for P300 spelling (90% vs 91%). Significance. These results show that camera-derived VEOG can act as a useful ocular reference for EEG artefact suppression without additional periocular electrodes. The validation was conducted on controlled recordings and should be interpreted as a best-case demonstration rather than a full validation in unconstrained wearable settings.