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

<i>Objective.</i>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-oriented<i>Gabor</i>or<i>Ricker</i>patches that optimize foveal neural response while reducing peripheral distraction.<i>Approach.</i>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: traditional<i>Plain</i>flickers,<i>Gabor</i>-based, or<i>Ricker</i>-based flickers. These flickers were part of a five-class code visually evoked potentials paradigm featuring low frequency, short, and aperiodic visual flashes.<i>Main results.</i>Subjective ratings revealed that<i>Gabor</i>and<i>Ricker</i>stimuli were visually comfortable and nearly invisible in peripheral vision compared to plain flickers. Moreover,<i>Gabor</i>and<i>Ricker</i>-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.<i>Significance.</i>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.