Author(s): Yang, Chen; Yan, Xinyi; Wang, Yijun; Chen, Yonghao; Zhang, Hongxin; Gao, Xiaorong

Source: JOURNAL OF NEURAL ENGINEERING Volume: 18 Issue: 4 Article Number: 0460b7 DOI: 10.1088/1741-2552/ac127f Published: AUG 2021

Abstract: Objective. Asynchronous brain-computer interfaces (BCIs) show significant advantages in many practical application scenarios. Compared with the rapid development of synchronous BCIs technology, the progress of asynchronous BCI research, in terms of containing multiple targets and training-free detection, is still relatively slow. In order to improve the practicability of the BCI, a spatio-temporal equalization multi-window algorithm (STE-MW) was proposed for asynchronous detection of steady-state visual evoked potential (SSVEP) without the need for acquiring calibration data. Approach. The algorithm used SIE strategy to intercept EEG signals of different lengths through multiple stacked time windows and statistical decisions-making based on Bayesian risk decision-making. Different from the traditional asynchronous algorithms based on the 'non-control state detection' methods, this algorithm was based on the 'statistical inspection-rejection decision' mode and did not require a separate classification of non-control states, so it can be effectively applied to detections for large-scale candidates. Main results. Online experimental results involving 14 healthy subjects showed that, in the continuously input experiments of 40 targets, the algorithm achieved the average recognition accuracy of 97.2 +/- 2.6% Accession Number: WOS:000678604000001

ISSN: 1741-2560

eISSN: 1741-2552

Full Text: https://iopscience.iop.org/article/10.1088/1741-2552/ac127f