AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (1.9 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) of Chinese speller for a patient with amyotrophic lateral sclerosis: A case report

Nanlin Shi1Liping Wang2Yonghao Chen1Xinyi Yan1Chen Yang1Yijun Wang3Xiaorong Gao3( )
Biomedical Engineering Department, Tsinghua University, Beijing 100084, China;
Neurology Department, Peking University Third Hospital, Beijing 100191, China;
State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
Show Author Information

Abstract

This study applied a steady-state visual evoked potential (SSVEP) based brain–computer interface (BCI) to a patient in lock-in state with amyotrophic lateral sclerosis (ALS) and validated its feasibility for communication. The developed calibration-free and asynchronous spelling system provided a natural and efficient communication experience for the patient, achieving a maximum free-spelling accuracy above 90% and an information transfer rate of over 22.203 bits/min. A set of standard frequency scanning and task spelling data were also acquired to evaluate the patient’s SSVEP response and to facilitate further personalized BCI design. The results demonstrated that the proposed SSVEP-based BCI system was practical and efficient enough to provide daily life communication for ALS patients.

References

[1]
EW Sellers, E Donchin. A P300-based brain-computer interface: initial tests by ALS patients. Clin Neurophysiol. 2006, 117(3): 538-548.
[2]
S Silvoni, C Volpato, M Cavinato, et al. P300-based brain-computer interface communication: evaluation and follow-up in amyotrophic lateral sclerosis. Front Neurosci. 2009, 3: 60.
[3]
JN Mak, DJ McFarland, TM Vaughan, et al. EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis. J Neural Eng. 2012, 9(2): 026014.
[4]
L Botrel, EM Holz, A Kübler. Using brain painting at home for 5 years: stability of the P300 during prolonged BCI usage by two end-users with ALS. In Augmented Cognition. Enhancing Cognition and Behavior in Complex Human Environments. D Schmorrow, C Fidopiastis, Eds. Cham: Springer, 2017.
[5]
MJ Vansteensel, EGM Pels, MG Bleichner, et al. Fully implanted brain-computer interface in a locked-in patient with ALS. N Engl J Med. 2016, 375(21): 2060-2066.
[6]
T Milekovic, AA Sarma, D Bacher, et al. Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals. J Neurophysiol. 2018, 120(1): 343-360.
[7]
D Lesenfants, D Habbal, Z Lugo, et al. An independent SSVEP-based brain-computer interface in locked-in syndrome. J Neural Eng. 2014, 11(3): 035002.
[8]
HJ Hwang, CH Han, JH Lim, et al. Clinical feasibility of brain-computer interface based on steady-state visual evoked potential in patients with locked-in syndrome: Case studies. Psychophysiology. 2017, 54(3): 444-451.
[9]
XG Chen, YJ Wang, M Nakanishi, et al. Hybrid frequency and phase coding for a high-speed SSVEP- based BCI speller. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago, IL, 2014.
[10]
DH Brainard. The psychophysics toolbox. Spat Vis. 1997, 10(4): 433-436.
[11]
RC Panicker, S Puthusserypady, Y Sun. An asynchronous P300 BCI with SSVEP-based control state detection. IEEE Trans Biomed Eng. 2011, 58(6): 1781-1788.
[12]
C Yang, X Han, YJ Wang, et al. A dynamic window recognition algorithm for SSVEP-based brain-computer interfaces using a spatio-temporal equalizer. Int J Neural Syst. 2018, 28(10): 1850028.
[13]
A Kübler, N Neumann, J Kaiser, et al. Brain-computer communication: self-regulation of slow cortical potentials for verbal communication. Arch Phys Med Rehabil. 2001, 82(11): 1533-1539.
[14]
LM McCane, SM Heckman, DJ McFarland, et al. P300-based brain-computer interface (BCI) event- related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls. Clin Neurophysiol. 2015, 126(11): 2124-2131.
[15]
U Hoffmann, JM Vesin, T Ebrahimi, et al. An efficient P300-based brain-computer interface for disabled subjects. J Neurosci Methods. 2008, 167(1): 115-125.
[16]
YJ Wang, XG Chen, XR Gao, et al. A benchmark dataset for SSVEP-based brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng. 2017, 25(10): 1746-1752.
[17]
DE Thompson, S Blain-Moraes, JE Huggins. Performance assessment in brain-computer interface-based augmentative and alternative communication. Biomed Eng Online. 2013, 12: 43.
[18]
DH Zhu, J Bieger, G Garcia Molina, et al. A survey of stimulation methods used in SSVEP-based BCIs. Comput Intell Neurosci. 2010: 702357.
[19]
AM Norcia, LG Appelbaum, JM Ales, et al. The steady-state visual evoked potential in vision research: a review. J Vis. 2015, 15(6): 4.
[20]
H Nezamfar, SS Mohseni Salehi, M Higger, et al. Code-VEP vs. eye tracking: a comparison study. Brain Sci. 2018, 8(7): E130.
Journal of Neurorestoratology
Pages 40-52
Cite this article:
Shi N, Wang L, Chen Y, et al. Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) of Chinese speller for a patient with amyotrophic lateral sclerosis: A case report. Journal of Neurorestoratology, 2020, 8(1): 40-52. https://doi.org/10.26599/JNR.2020.9040003

883

Views

107

Downloads

18

Crossref

17

Web of Science

0

Scopus

Altmetrics

Received: 19 January 2020
Revised: 07 February 2020
Accepted: 13 February 2020
Published: 05 March 2020
© The authors 2020

This article is published with open access at http://jnr.tsinghuajournals.com

Return