Literature DB >> 27542113

Optimization of Checkerboard Spatial Frequencies for Steady-State Visual Evoked Potential Brain-Computer Interfaces.

Nicholas R Waytowich, Yusuke Yamani, Dean J Krusienski.   

Abstract

Steady-state visual evoked potentials (SSVEPs) are oscillations of the electroencephalogram (EEG) which are mainly observed over the occipital area that exhibit a frequency corresponding to a repetitively flashing visual stimulus. SSVEPs have proven to be very consistent and reliable signals for rapid EEG-based brain-computer interface (BCI) control. There is conflicting evidence regarding whether solid or checkerboard-patterned flashing stimuli produce superior BCI performance. Furthermore, the spatial frequency of checkerboard stimuli can be varied for optimal performance. The present study performs an empirical evaluation of performance for a 4-class SSVEP-based BCI when the spatial frequency of the individual checkerboard stimuli is varied over a continuum ranging from a solid background to single-pixel checkerboard patterns. The results indicate that a spatial frequency of 2.4 cycles per degree can maximize the information transfer rate with a reduction in subjective visual irritation compared to lower spatial frequencies. This important finding on stimulus design can lead to improved performance and usability of SSVEP-based BCIs.

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Year:  2016        PMID: 27542113     DOI: 10.1109/TNSRE.2016.2601013

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  5 in total

1.  Improving user experience of SSVEP BCI through low amplitude depth and high frequency stimuli design.

Authors:  S Ladouce; L Darmet; J J Torre Tresols; S Velut; G Ferraro; F Dehais
Journal:  Sci Rep       Date:  2022-05-25       Impact factor: 4.996

2.  An Adaptive Calibration Framework for mVEP-Based Brain-Computer Interface.

Authors:  Teng Ma; Fali Li; Peiyang Li; Dezhong Yao; Yangsong Zhang; Peng Xu
Journal:  Comput Math Methods Med       Date:  2018-02-26       Impact factor: 2.238

3.  Steady-State Visual Evoked Potential-Based Brain-Computer Interface Using a Novel Visual Stimulus with Quick Response (QR) Code Pattern.

Authors:  Nannaphat Siribunyaphat; Yunyong Punsawad
Journal:  Sensors (Basel)       Date:  2022-02-13       Impact factor: 3.576

4.  A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response.

Authors:  Lu Jiang; Xiaoyang Li; Weihua Pei; Xiaorong Gao; Yijun Wang
Journal:  Front Hum Neurosci       Date:  2022-02-03       Impact factor: 3.169

5.  Performance Evaluation of Visual Noise Imposed Stochastic Resonance Effect on Brain-Computer Interface Application: A Comparison Between Motion-Reversing Simple Ring and Complex Checkerboard Patterns.

Authors:  Jun Xie; Guangjing Du; Guanghua Xu; Xingang Zhao; Peng Fang; Min Li; Guozhi Cao; Guanglin Li; Tao Xue; Yanjun Zhang
Journal:  Front Neurosci       Date:  2019-11-08       Impact factor: 4.677

  5 in total

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