Almost free of calibration for SSVEP-based brain-computer interfaces

Ruixin Luo; Xiaolin Xiao; Enze Chen; Lin Meng; Tzyy-Ping Jung; Minpeng Xu; Dong Ming

doi:10.1088/1741-2552/ad0b8f

Almost free of calibration for SSVEP-based brain-computer interfaces

J Neural Eng. 2023 Nov 22;20(6). doi: 10.1088/1741-2552/ad0b8f.

Authors

Ruixin Luo^{1

2}, Xiaolin Xiao^{1

2

3}, Enze Chen¹, Lin Meng^{1

3}, Tzyy-Ping Jung^{1

2

4}, Minpeng Xu^{1

2

3}, Dong Ming^{1

2

3}

Affiliations

¹ Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China.
² College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, People's Republic of China.
³ Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, People's Republic of China.
⁴ The Swartz Center for Computational Neuroscience, University of California, San Diego, CA, United States of America.

PMID: 37948768
DOI: 10.1088/1741-2552/ad0b8f

Abstract

Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is a promising technology that can achieve high information transfer rate (ITR) with supervised algorithms such as ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA). However, training individual models requires a tedious and time-consuming calibration process, which hinders the real-life use of SSVEP-BCIs. A recent data augmentation method, called source aliasing matrix estimation (SAME), can generate new EEG samples from a few calibration trials. But SAME does not exploit the information across stimuli as well as only reduces the number of calibration trials per command, so it still has some limitations.Approach. This study proposes an extended version of SAME, called multi-stimulus SAME (msSAME), which exploits the similarity of the aliasing matrix across frequencies to enhance the performance of SSVEP-BCI with insufficient calibration trials. We also propose a semi-supervised approach based on msSAME that can further reduce the number of SSVEP frequencies needed for calibration. We evaluate our method on two public datasets, Benchmark and BETA, and an online experiment.Main results. The results show that msSAME outperforms SAME for both eTRCA and TDCA on the public datasets. Moreover, the semi-supervised msSAME-based method achieves comparable performance to the fully calibrated methods and outperforms the conventional free-calibrated methods. Remarkably, our method only needs 24 s to calibrate 40 targets in the online experiment and achieves an average ITR of 213.8 bits min^-1with a peak of 242.6 bits min^-1.Significance. This study significantly reduces the calibration effort for individual SSVEP-BCIs, which is beneficial for developing practical plug-and-play SSVEP-BCIs.

Keywords: brain–computer interface (BCI); ensemble task-related component analysis (eTRCA); source aliasing matrix estimation (SAME); steady-state visual evoked potential (SSVEP); task-discriminant component analysis (TDCA).

Publication types

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

MeSH terms

Algorithms
Brain-Computer Interfaces*
Calibration
Electroencephalography / methods
Evoked Potentials, Visual*
Photic Stimulation / methods