title: Effect of higher frequency on the classification of steady-state visual evoked potentials.
abstract: OBJECTIVE
  Most existing brain-computer interface (BCI) designs based on steady-state visual evoked potentials (SSVEPs) primarily use low frequency visual stimuli (e.g., <20 Hz) to elicit relatively high SSVEP amplitudes. While low frequency stimuli could evoke photosensitivity-based epileptic seizures, high frequency stimuli generally show less visual fatigue and no stimulus-related seizures. The fundamental objective of this study was to investigate the effect of stimulation frequency and duty-cycle on the usability of an SSVEP-based BCI system.
  
  
  METHODS
  We developed an SSVEP-based BCI speller using multiple LEDs flickering with low frequencies (6-14.9 Hz) with a duty-cycle of 50%, or higher frequencies (26-34.7 Hz) with duty-cycles of 50%, 60%, and 70%. The four different experimental conditions were tested with 26 subjects in order to investigate the impact of stimulation frequency and duty-cycle on performance and visual fatigue, and evaluated with a questionnaire survey. Resting state alpha powers were utilized to interpret our results from the neurophysiological point of view.
  
  
  RESULTS
  The stimulation method employing higher frequencies not only showed less visual fatigue, but it also showed higher and more stable classification performance compared to that employing relatively lower frequencies. Different duty-cycles in the higher frequency stimulation conditions did not significantly affect visual fatigue, but a duty-cycle of 50% was a better choice with respect to performance. The performance of the higher frequency stimulation method was also less susceptible to resting state alpha powers, while that of the lower frequency stimulation method was negatively correlated with alpha powers.
  
  
  CONCLUSIONS
  These results suggest that the use of higher frequency visual stimuli is more beneficial for performance improvement and stability as time passes when developing practical SSVEP-based BCI applications.
authors.affiliation: Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul, Korea.
authors.forename: Dong-Ok
authors.initials: DO
authors.lastname: Won
authors.forename: Han-Jeong
authors.initials: HJ
authors.lastname: Hwang
authors.forename: Sven
authors.initials: S
authors.lastname: Dähne
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.forename: Seong-Whan
authors.initials: SW
authors.lastname: Lee
mesh_terms: D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D005425:Flicker Fusion / Q000502:physiology; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D010775:Photic Stimulation / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted