Dataset of binocularly coded steady-state visual evoked potentials recorded with an augmented reality headset

Area of Science:

• Neuroscience
• Computer Science
• Human-Computer Interaction

Background:

• Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer practical applications.
• Integrating SSVEP-BCIs with augmented reality (AR) displays promises portable systems, but empirical data is limited, especially for novel stimulation methods.

Purpose of the Study:

• To present a curated SSVEP dataset using a binocular AR headset and lightweight electroencephalography (EEG) recorder.
• To explore binocular-incongruent dual-frequency stimulation, contrasting it with conventional binocular-congruent protocols.
• To analyze SSVEP characteristics and BCI performance under different stimulation conditions, including inter-ocular frequency and phase disparities.

Main Methods:

• Recorded SSVEP data using a custom AR headset with independent control of visual input to each eye.
• Implemented both binocular-congruent single-frequency and binocular-incongruent dual-frequency stimulation paradigms.
• Performed comparative analyses of SSVEP features and BCI performance metrics between the stimulation protocols.

Main Results:

• Demonstrated the feasibility of wearable AR-SSVEP-BCIs.
• Showcased binocular-incongruent dual-frequency stimulation as an effective strategy for enhancing target separability.
• Quantified the impact of inter-ocular frequency and phase differences on SSVEP responses and BCI accuracy.

Conclusions:

• Wearable AR-SSVEP-BCIs are viable, with binocular-incongruent stimulation offering advantages.
• The presented dataset facilitates research into portable BCIs, advanced encoding, and binocular vision mechanisms.
• This work advances the development of next-generation brain-computer interfaces for diverse applications.