TY - JOUR
T1 - Phase-Approaching Stimulation Sequence for SSVEP-Based BCI
T2 - A Practical Use in VR/AR HMD
AU - Hsu, Hao Teng
AU - Shyu, Kuo Kai
AU - Hsu, Chuan Chih
AU - Lee, Lung Hao
AU - Lee, Po Lei
N1 - Funding Information:
Thiswork was supported by the National Central University, Ministry of Science and Technology (MOST) through Pervasive Artificial Intelligence Research (PAIR) Labs under Grant 110-2634-F-008-007- and Grant 109-2221-E-008-074-.
Publisher Copyright:
© 2001-2011 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - brain computer interface
KW - canonical correlation accuracy
KW - Phase-approaching method
KW - steady state visual evoked potential
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U2 - 10.1109/TNSRE.2021.3131779
DO - 10.1109/TNSRE.2021.3131779
M3 - Article
C2 - 34847036
AN - SCOPUS:85123555661
SN - 1534-4320
VL - 29
SP - 2754
EP - 2764
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
ER -