A Single-Stimulus, Multitarget BCI Based on Retinotopic Mapping of Motion-Onset VEPs.

Objective: We present a new type of brain-computer interface (BCI) that utilizes the retinotopic mapping of motion-onset visual evoked potentials (mVEP) to accomplish four control channels using a single motion stimulus.
Methods: Participants selected a BCI command by fixating one of four target locations around a centrally presented visual motion stimulus. A template-matching method was employed to recognize the users' intention by decoding the position of the motion stimulus in the peripheral visual field, and classification performances were evaluated in an offline manner. The motion stimulus eccentricity between the targets and the visual motion stimulus varied among 5.1°, 6.7°, 9.8°, and 13.0°.
Results: Distinct N200 spatial patterns were elicited when participants directed attention overtly to the target locations. A four-class classification accuracy of 72.2 ± 5.05% was achieved with a distance of 5.1° visual angle between the targets and the visual motion stimulus. The classification accuracies decreased with increasing motion stimulus eccentricities but remained separable well above the chance level at 13.0° (47.3 ± 8.54%).
Conclusion: Our results support the feasibility of a single-stimulus, multitarget mVEP BCI.
Significance: The proposed system can simplify the visual stimulation of mVEP BCIs, improve user experience and pave the way for simple yet efficient BCI communication.