An Open Steady-State Visually Evoked Potentials dataset for Augmented Reality-based Brain-Computer Interfaces

An open benchmark dataset related to Steady-State Visually Evoked Potential (SSVEP) signals induced by means of an augmented reality (AR) Head-Mounted Display (HMD) is presented. This benchmark dataset consists of 8-channel electroencephalogram (EEG) data acquired from 30 healthy subjects (15 experienced and 15 naive) while they performed a cue-guided target selection task. Eight concurrent visual flickers, encoded using a joint frequency and phase modulation (JFPM) approach, were accommodated within the field of view of the AR HMD worn by the subjects. The stimulation frequencies ranged from 8 Hz to 15 Hz with an interval of 1 Hz, while the phase difference between two adjacent flickers was set to pi\/2. The dataset comprises five acquisition cycles for each subject, with each cycle corresponding to signals recorded from all eight flickers, presented by the visual cues in a randomized order. The stimulation duration in each selection task was five seconds. To ensure the dataset's trustworthiness, two crucial parameters for effective stimulus recognition were monitored and controlled, namely the illuminance of the environment and the frames per second variation of the AR device, which are primary factors influencing the performance of AR-based SSVEP Brain-Computer Interfaces (BCIs). Additionally, the BCI instrument acquired acceleration and angular velocity data related to the subjects' head movements. In this way, the proposed dataset addresses the gap in the availability of public datasets for AR-based SSVEP BCIs, serving as a benchmark for comparing different algorithms in i) target identification, and ii) motion artifact detection. This contribution is crucial for advancing the development of wearable and robust BCI systems aligned with the human-centric principles of the Industry 5.0 paradigm.