Brain-computer interface coding method based on high-frequency dual-frequency SSVEP
10.3760/cma.j.cn121382-20230525-00402
- VernacularTitle:基于高频双频SSVEP的脑-机接口编码方法研究
- Author:
Xiaohe CHEN
1
;
Yufeng KE
;
Wei XU
;
Dong MING
Author Information
1. 天津大学医学工程与转化医学研究院，天津 300072
- Keywords:
Brain-computer interface;
High-frequency steady-state visual evoked potential;
Dual-frequency coding;
Electroencephalography
- From:
International Journal of Biomedical Engineering
2023;46(4):288-299
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To improve the users’ comfort of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) through high-frequency stimulation and overcome the problem of accuracy decline caused by high frequency by combining dual-frequency encoding.Methods:Two dual-frequency high-frequency 60-instruction paradigms based on left and right visual fields and checkerboard stimuli were designed based on the 25.5 - 39.6 Hz frequency. Thirteen subjects participated in the experiment, and spectrum and spatial characteristics analyses were performed on SSVEP signals. The filter bank parameters were optimized based on the spectrum characteristics. Extended canonical correlation analysis (eCCA), ensemble task-related component analysis (eTRCA), and task-discriminant component analysis (TDCA) were used for SSVEP recognition.Results:Stable SSVEP was successfully induced in both the left and right visual fields and the checkerboard grid paradigm. The left and right visual fields had high signal-to-noise ratios for the fundamental frequency and its harmonics and weak signal-to-noise ratios for intermodulation components, whereas the intermodulation components of the 2 stimulus frequencies of the checkerboard grid, f1 + f2, had significantly higher signal-to-noise ratios than the second harmonic components above 30 Hz, and there was also a f2 ? f1 component and a 2 f1 ? f2 component. Combined with brain topography, it can be seen that the f1 and f2 response components of the left and right visual fields are located on opposite sides of the visual field, while the checkerboard grids are both concentrated in the center of the occipital region. Regarding the lateralization of brain topography amplitude and signal-to-noise ratio, the mean values of the PO3 and PO4 signal-to-noise ratios at the stimulation frequency of the left and right visual fields are consistent with the contralateral response characteristics. The 5 fb ? 1 method is the optimal filter set setting method, and the recognition correctness rate of TDCA for the left and right visual fields is the highest. However, the comparison of the recognition correctness rate of tessellated lattice eTRCA and TDCA is not statistically significant ( P > 0.05). The information transmission rates of the three algorithms all increase and then decrease with the increase in data length. Conclusions:The designed dual-frequency, high-frequency SSVEP-BCI paradigm is able to better balance performance and comfort and provides a basis for practical large instruction set BCI design methods.
