Phase-Approaching Stimulation Sequence for SSVEP-Based BCI: A Practical Use in VR/AR HMD

Abstract

Abstract-Steady-state visual evoked potential (SSVEP) has been used to implement brain-computer interface (BCI) due to its advantages of high information transfer rate (ITR) and high accuracy. In recent years, owing to the developments of head-mounted device (HMD), the HMD has become a popular device to implement SSVEP-based BCI. However, an HMD with fixed frame rate only can flash at its subharmonic frequencies which limits the available number of stimulation frequencies for SSVEP-based BCI. In order to increase the number of available commands for SSVEP-based BCI, we proposed a phase-approaching (PA) method to generate visual stimulation sequences at user-specified frequency on an HMD. The flickering sequence generated by our PA method (PAS sequence) tries to approximate user-specified stimulation frequency by means of minimizing the difference of accumulated phases between our PAS sequence and the ideal wave of user-specified frequency. The generated sequence of PA method determines the brightness state for each frame to approach the accumulated phase of the ideal wave. The SSVEPs evoked from stimulators, driven by PAS sequences, were analyzed using canonical correlation analysis (CCA) to identify user's gazed target. In this study, a six-command SSVEP-based BCI was designed to operate a flying drone. The ITR and detection accuracy are 36.84 bits/min and 93.30%, respectively.

Overview of the winning approaches in 2022 World Robot Contest Championship–Asynchronous SSVEP

“…However, these methods may require a long stimulation duration to achieve high accuracy, leading to a low ITR. Furthermore, training-based approaches, such as extended CCA (eCCA) [16], modified extended CCA (m-extended CCA) [11], L1-regularized multiway CCA (L1MCCA) [17], task-related component analysis (TRCA) [18], and task-discriminant component analysis (TDCA) [19], use calibration data to construct specific templates and spatial filters for the subjects, resulting in improved performance of SSVEP-based BCI systems.…”

Humanoid Robot Walking in Maze Controlled by SSVEP-BCI Based on Augmented Reality Stimulus

“…The importability of the visual stimulation devices led to the inconvenience for the robot controlling, which required the improvement of a stimulator in portability and intelligence. Fortunately, augmented reality (AR) technology provided a new visual stimuli mode in the virtual scenes, which could be used to improve the convenience and practicability of the BCI system for robot controlling ( Appaia et al, 2021 ; Hsu et al, 2021 ). At present, the studies of BCIs based on AR stimulators are in its infancy, and the feasibility of this scheme has been verified by some experiments.…”