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Journal of Neurorestoratology 2020, 8(1): 40-52 https://doi.org/10.26599/JNR.2020.9040003
Research Article | Open Access | Issue | Published: 05 March 2020

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

Show Author's Information Nanlin Shi1,  Liping Wang2,  Yonghao Chen1,  Xinyi Yan1,  Chen Yang1,  Yijun Wang3,  Xiaorong 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
Keywords:
steady-state visual evoked potential (SSVEP), brain–computer interface (BCI), amyotrophic lateral sclerosis (ALS)
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
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Abstract Full text About this article

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.


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About this article

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

Show Author's information Nanlin Shi1, Liping Wang2, Yonghao Chen1, Xinyi Yan1, Chen Yang1, Yijun Wang3, Xiaorong 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

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.

Keywords: steady-state visual evoked potential (SSVEP), brain–computer interface (BCI), amyotrophic lateral sclerosis (ALS)

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Publication history
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Publication history

Received: 19 January 2020
Revised: 07 February 2020
Accepted: 13 February 2020
Published: 05 March 2020
Issue date: March 2020

Copyright

© The authors 2020

Acknowledgements

The authors would like to pay gratitude to the subject and his parents for their patient cooperation. This work was supported by the Key Clinical Projects of Peking University Third Hospital (No.Y76437-01), National Key Research and Development Program of China (No. 2017YFB1002505), National Natural Science Foundation of China under Grant (No. 61431007), Key Research and Development Program of Guangdong Province (No. 2018B030339001), and Doctoral Brain+X Seed Grand Program of Tsinghua University.

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