Leveraging textured flickers: a leap toward practical, visually comfortable, and high-performance dry EEG code-VEP BCI.

Objective.Reactive brain-computer interfaces typically rely on repetitive visual stimuli, which can strain the eyes and cause attentional distraction. To address these challenges, we propose a novel approach rooted in visual neuroscience to design visual Stimuli for Augmented Response (StAR). The StAR stimuli consist of small randomly-orientedGabororRickerpatches that optimize foveal neural response while reducing peripheral distraction.Approach.In a factorial design study, 24 participants equipped with an 8-dry electrode EEG system focused on series of target flickers presented under three formats: traditionalPlainflickers,Gabor-based, orRicker-based flickers. These flickers were part of a five-class code visually evoked potentials paradigm featuring low frequency, short, and aperiodic visual flashes.Main results.Subjective ratings revealed thatGaborandRickerstimuli were visually comfortable and nearly invisible in peripheral vision compared to plain flickers. Moreover,GaborandRicker-based textures achieved higher accuracy (93.6% and 96.3%, respectively) with only 88 s of calibration data, compared to plain flickers (65.6%). A follow-up online implementation of this experiment was conducted to validate our findings within the frame of naturalistic operations. During this trial, remarkable accuracies of 97.5% in a cued task and 94.3% in an asynchronous digicode task were achieved, with a mean decoding time as low as 1.68 s.Significance.This work demonstrates the potential to expand BCI applications beyond the lab by integrating visually unobtrusive systems with gel-free, low density EEG technology, thereby making BCIs more accessible and efficient. The datasets, algorithms, and BCI implementations are shared through open-access repositories.