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Research Article

High-speed spelling with a noninvasive brain–computer interface

Xiaogang Chen, Yijun Wang, Masaki Nakanishi, Xiaorong Gao, Tzyy-Ping Jung, and Shangkai Gao
PNAS November 3, 2015 112 (44) E6058-E6067; first published October 19, 2015; https://doi.org/10.1073/pnas.1508080112
Xiaogang Chen
aDepartment of Biomedical Engineering, Tsinghua University, Beijing 100084, China;
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Yijun Wang
bSwartz Center for Computational Neuroscience, University of California, San Diego, CA 92093;
cState Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
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  • For correspondence: wangyj@semi.ac.cn gxr-dea@tsinghua.edu.cn
Masaki Nakanishi
bSwartz Center for Computational Neuroscience, University of California, San Diego, CA 92093;
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Xiaorong Gao
aDepartment of Biomedical Engineering, Tsinghua University, Beijing 100084, China;
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  • For correspondence: wangyj@semi.ac.cn gxr-dea@tsinghua.edu.cn
Tzyy-Ping Jung
bSwartz Center for Computational Neuroscience, University of California, San Diego, CA 92093;
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Shangkai Gao
aDepartment of Biomedical Engineering, Tsinghua University, Beijing 100084, China;
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  1. Edited by Terrence J. Sejnowski, Salk Institute for Biological Studies, La Jolla, CA, and approved September 16, 2015 (received for review April 24, 2015)

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Significance

Brain–computer interface (BCI) technology provides a new communication channel. However, current applications have been severely limited by low communication speed. This study reports a noninvasive brain speller that achieved a multifold increase in information transfer rate compared with other existing systems. Based on extremely precise coding of frequency and phase in single-trial steady-state visual evoked potentials, this study developed a new joint frequency-phase modulation method and a user-specific decoding algorithm to implement synchronous modulation and demodulation of electroencephalograms. The resulting speller obtained high spelling rates up to 60 characters (∼12 words) per minute. The proposed methodological framework of high-speed BCI can lead to numerous applications in both patients with motor disabilities and healthy people.

Abstract

The past 20 years have witnessed unprecedented progress in brain–computer interfaces (BCIs). However, low communication rates remain key obstacles to BCI-based communication in humans. This study presents an electroencephalogram-based BCI speller that can achieve information transfer rates (ITRs) up to 5.32 bits per second, the highest ITRs reported in BCI spellers using either noninvasive or invasive methods. Based on extremely high consistency of frequency and phase observed between visual flickering signals and the elicited single-trial steady-state visual evoked potentials, this study developed a synchronous modulation and demodulation paradigm to implement the speller. Specifically, this study proposed a new joint frequency-phase modulation method to tag 40 characters with 0.5-s-long flickering signals and developed a user-specific target identification algorithm using individual calibration data. The speller achieved high ITRs in online spelling tasks. This study demonstrates that BCIs can provide a truly naturalistic high-speed communication channel using noninvasively recorded brain activities.

  • brain–computer interface
  • electroencephalogram
  • steady-state visual evoked potentials
  • joint frequency-phase modulation

Footnotes

  • ↵1X.C. and Y.W. contributed equally to this work.

  • ↵2To whom correspondence may be addressed. Email: wangyj{at}semi.ac.cn or gxr-dea{at}tsinghua.edu.cn.
  • Author contributions: Y.W. and X.G. designed research; X.C. and Y.W. performed research; X.C., Y.W., and M.N. analyzed data; and X.C., Y.W., M.N., X.G., T.-P.J., and S.G. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1508080112/-/DCSupplemental.

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Spelling with a brain–computer interface
Xiaogang Chen, Yijun Wang, Masaki Nakanishi, Xiaorong Gao, Tzyy-Ping Jung, Shangkai Gao
Proceedings of the National Academy of Sciences Nov 2015, 112 (44) E6058-E6067; DOI: 10.1073/pnas.1508080112

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Spelling with a brain–computer interface
Xiaogang Chen, Yijun Wang, Masaki Nakanishi, Xiaorong Gao, Tzyy-Ping Jung, Shangkai Gao
Proceedings of the National Academy of Sciences Nov 2015, 112 (44) E6058-E6067; DOI: 10.1073/pnas.1508080112
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