Literature DB >> 21421448

BCI demographics II: how many (and what kinds of) people can use a high-frequency SSVEP BCI?

Ivan Volosyak1, Diana Valbuena, Thorsten Lüth, Tatsiana Malechka, Axel Gräser.   

Abstract

Brain-computer interface (BCI) systems use brain activity as an input signal and enable communication without movement. This study is a successor of our previous study (BCI demographics I) and examines correlations among BCI performance, personal preferences, and different subject factors such as age or gender for two sets of steady-state visual evoked potential (SSVEP) stimuli: one in the medium frequency range (13, 14, 15 and 16 Hz) and another in the high-frequency range (34, 36, 38, 40 Hz). High-frequency SSVEPs (above 30 Hz) diminish user fatigue and risk of photosensitive epileptic seizures. Results showed that most people, despite having no prior BCI experience, could use the SSVEP-based Bremen-BCI system in a very noisy field setting at a fair. Results showed that demographic parameters as well as handedness, tiredness, alcohol and caffeine consumption, etc., have no significant effect on the performance of SSVEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only five out of total 86 participants indicated change in fatigue during the experiment. 84 subjects performed with a mean information transfer rate of 17.24 ±6.99 bit/min and an accuracy of 92.26 ±7.82% with the medium frequency set, whereas only 56 subjects performed with a mean information transfer rate of 12.10 ±7.31 bit/min and accuracy of 89.16 ±9.29% with the high-frequency set. These and other demographic analyses may help identify the best BCI for each user.

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Year:  2011        PMID: 21421448     DOI: 10.1109/TNSRE.2011.2121919

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


  33 in total

1.  A multi-day and multi-band dataset for a steady-state visual-evoked potential-based brain-computer interface.

Authors:  Ga-Young Choi; Chang-Hee Han; Young-Jin Jung; Han-Jeong Hwang
Journal:  Gigascience       Date:  2019-11-01       Impact factor: 6.524

2.  The performance of 9-11-year-old children using an SSVEP-based BCI for target selection.

Authors:  James J S Norton; Jessica Mullins; Birgit E Alitz; Timothy Bretl
Journal:  J Neural Eng       Date:  2018-06-28       Impact factor: 5.379

Review 3.  Guidelines for Feature Matching Assessment of Brain-Computer Interfaces for Augmentative and Alternative Communication.

Authors:  Kevin M Pitt; Jonathan S Brumberg
Journal:  Am J Speech Lang Pathol       Date:  2018-08-06       Impact factor: 2.408

4.  Studying modulation on simultaneously activated SSVEP neural networks by a cognitive task.

Authors:  Zhenghua Wu
Journal:  J Biol Phys       Date:  2014-01-13       Impact factor: 1.365

Review 5.  Brain-Computer Interfaces for Augmentative and Alternative Communication: A Tutorial.

Authors:  Jonathan S Brumberg; Kevin M Pitt; Alana Mantie-Kozlowski; Jeremy D Burnison
Journal:  Am J Speech Lang Pathol       Date:  2018-02-06       Impact factor: 2.408

Review 6.  Review of Riemannian Distances and Divergences, Applied to SSVEP-based BCI.

Authors:  S Chevallier; E K Kalunga; Q Barthélemy; E Monacelli
Journal:  Neuroinformatics       Date:  2021-01

7.  Risk factors of fatigue status among Chinese adolescents.

Authors:  Yuelong Jin; Baozhen Peng; Yijun Li; Lei Song; Lianping He; Rui Fu; Qianqian Wu; Qingxiu Fan; Yingshui Yao
Journal:  Int J Clin Exp Med       Date:  2015-10-15

8.  A Usability Study of Low-cost Wireless Brain-Computer Interface for Cursor Control Using Online Linear Model.

Authors:  Reza Abiri; Soheil Borhani; Justin Kilmarx; Connor Esterwood; Yang Jiang; Xiaopeng Zhao
Journal:  IEEE Trans Hum Mach Syst       Date:  2020-05-14       Impact factor: 2.968

9.  Performance assessment in brain-computer interface-based augmentative and alternative communication.

Authors:  David E Thompson; Stefanie Blain-Moraes; Jane E Huggins
Journal:  Biomed Eng Online       Date:  2013-05-16       Impact factor: 2.819

10.  Assisted closed-loop optimization of SSVEP-BCI efficiency.

Authors:  Jacobo Fernandez-Vargas; Hanns U Pfaff; Francisco B Rodríguez; Pablo Varona
Journal:  Front Neural Circuits       Date:  2013-02-25       Impact factor: 3.492

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