A 120-target brain-computer interface based on code-modulated visual evoked potentials

Qingyu Sun; Li Zheng; Weihua Pei; Xiaorong Gao; Yijun Wang

doi:10.1016/j.jneumeth.2022.109597

A 120-target brain-computer interface based on code-modulated visual evoked potentials

J Neurosci Methods. 2022 Jun 1:375:109597. doi: 10.1016/j.jneumeth.2022.109597. Epub 2022 Apr 12.

Authors

Qingyu Sun¹, Li Zheng¹, Weihua Pei¹, Xiaorong Gao², Yijun Wang³

Affiliations

¹ State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
² Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
³ State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China; Chinese Institute for Brain Research, Beijing 102206, China. Electronic address: wangyj@semi.ac.cn.

PMID: 35427686
DOI: 10.1016/j.jneumeth.2022.109597

Abstract

Background: In recent years, numerous studies on the brain-computer interface (BCI) have been published. However, the number of targets in most of the existing studies was not enough for many practical applications.

New method: To achieve highly efficient communications, this study proposed a 120-target BCI system based on code-modulated visual evoked potentials (c-VEPs). Four 31-bit pseudorandom codes were used, and each code generated 30 targets by cyclic shift with a lag of 1 bit.

Results: In the online experiments, subjects could select one target in 1.04 s (0.52 s for stimulation and 0.52 s for gaze shifting) with an average information transfer rate (ITR) of 265.74 bits/min.

Comparison with existing method: The proposed system achieved more targets and higher ITR than other recent c-VEP based studies. which attributes to the optimal code combination and the 1-bit lag.

Conclusion: The results illustrate that the proposed BCI system can achieve a high ITR with a short stimulation time. In addition, the c-VEP paradigm can shorten the training time, which ensures practicality in real applications.

Keywords: Brain-computer interface (BCI); Code-modulated visual evoked potential (c-VEP); Electroencephalogram (EEG).

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Brain-Computer Interfaces*
Electroencephalography / methods
Evoked Potentials, Visual*
Humans
Neurologic Examination
Photic Stimulation / methods