title: Generating visual flickers for eliciting robust steady-state visual evoked potentials at flexible frequencies using monitor refresh rate.
abstract: In the study of steady-state visual evoked potentials (SSVEPs), it remains a challenge to present visual flickers at flexible frequencies using monitor refresh rate. For example, in an SSVEP-based brain-computer interface (BCI), it is difficult to present a large number of visual flickers simultaneously on a monitor. This study aims to explore whether or how a newly proposed frequency approximation approach changes signal characteristics of SSVEPs. At 10 Hz and 12 Hz, the SSVEPs elicited using two refresh rates (75 Hz and 120 Hz) were measured separately to represent the approximation and constant-period approaches. This study compared amplitude, signal-to-noise ratio (SNR), phase, latency, scalp distribution, and frequency detection accuracy of SSVEPs elicited using the two approaches. To further prove the efficacy of the approximation approach, this study implemented an eight-target BCI using frequencies from 8-15 Hz. The SSVEPs elicited by the two approaches were found comparable with regard to all parameters except amplitude and SNR of SSVEPs at 12 Hz. The BCI obtained an averaged information transfer rate (ITR) of 95.0 bits/min across 10 subjects with a maximum ITR of 120 bits/min on two subjects, the highest ITR reported in the SSVEP-based BCIs. This study clearly showed that the frequency approximation approach can elicit robust SSVEPs at flexible frequencies using monitor refresh rate and thereby can largely facilitate various SSVEP-related studies in neural engineering and visual neuroscience.
authors.affiliation: Graduate School of Science and Technology, Keio University, Yokohama, Kanagawa, Japan; Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America.
authors.forename: Masaki
authors.initials: M
authors.lastname: Nakanishi
authors.affiliation: Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America; Center for Advanced Neurological Engineering, Institute of Engineering in Medicine, University of California San Diego, La Jolla, California, United States of America.
authors.forename: Yijun
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America; Center for Advanced Neurological Engineering, Institute of Engineering in Medicine, University of California San Diego, La Jolla, California, United States of America.
authors.forename: Yu-Te
authors.initials: YT
authors.lastname: Wang
authors.affiliation: Graduate School of Science and Technology, Keio University, Yokohama, Kanagawa, Japan.
authors.forename: Yasue
authors.initials: Y
authors.lastname: Mitsukura
authors.affiliation: Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America; Center for Advanced Neurological Engineering, Institute of Engineering in Medicine, University of California San Diego, La Jolla, California, United States of America.
authors.forename: Tzyy-Ping
authors.initials: TP
authors.lastname: Jung
mesh_terms: D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005074:Evoked Potentials, Visual; D005425:Flicker Fusion; D006801:Humans