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Multi-Brain Coding Expands the Instruction Set in SSVEP-Based Brain-Computer Interfaces

delete2023-10-01
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PRE
AI
X
Xingxing Chu
于洋 cover
于洋 (Yang Yu)
K
Kaixuan Liu
Z
Zeqi Ye
D
Dewen Hu
L
Ling‐Li Zeng *
DOI:10.1109/THMS.2023.3273538delete
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Abstract

Abstract

En 中文
Previous studies have made great efforts to expand the instruction set in steady-state visual evoked potential (SSVEP)based brain-computer interfaces. However, most systems are limited to single persons and expand the instruction set by increasing the flicker stimulation frequency range or via multiple frequencies sequential coding or joint frequency/phase coding. In this article, we propose a multibrain coding SSVEP paradigm that encodes the SSVEP instructions generated by N independent subjects with M flicker stimuli, thus increasing the instruction set to MN instructions. A total of 40 subjects participated in online experiments in this article. The results show that there is no significant difference in accuracy (p > 0.05, paired t test) between the multibrain and single-brain coding systems, while the information transfer rate (ITR) increases significantly (p< 0.0001, paired t test), with theITR increasing from 37.66 +/- 3.60 bits/min for the single-brain coding system to 111.65 +/- 10.84 bits/min for multibrain coding system when N = 3 and M = 5. In summary, the proposed multibrain coding paradigm exponentially increases the number of instructions without increasing the output instruction time, which is of great significance to the application and promotion of BCIs.
Keywords:
Brain-computer interface (BCIs)
electroencephalography (EEG)
instruction set
multibrain coding
steady-state visual evoked potential (SSVEP)

Journal

IEEE Transactions on Human-Machine Systems cover
IEEE Transactions on Human-Machine Systems
IF:
4.4
Papers:
1.1K
Citations:
3.5K

Organization

N
national university of defense technology - china
Scholars:
1.8W
Papers: 1.4W
Citations: 9