An Asynchronous Training-Free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios

SSVEP-based brain-computer interface (BCI) systems have received a lot of attention due to their relatively high Signal to Noise Ratio (SNR) and less training requirements. Most of the existing steady-state visual evoked potential (SSVEP) detection algorithms treat the prior probability of each alte...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 4120 - 4130
Main Authors: Wang, Junsong, Cui, Yuntian, Zhang, Hongxin, Wu, Haolin, Yang, Chen
Format: Journal Article
Language:English
Published: United States IEEE 2024
Subjects:
Adult
Algorithms
asynchronous
Background noise
Brain modeling
Brain-computer interface (BCI)
Brain-Computer Interfaces
Detection Algorithms
Electroencephalography
Electroencephalography - methods
Evoked Potentials, Visual - physiology
Female
Humans
Male
Mathematical models
maximum a posteriori (MAP)
mutual information rate (MIR)
Photic Stimulation
Posterior probability
Probability
Probability distribution
Reproducibility of Results
Sensitivity and Specificity
Signal-To-Noise Ratio
steady-state visual evoked potential (SSVEP)
Target recognition
Training
Visualization
Young Adult
ISSN:1534-4320, 1558-0210, 1558-0210
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:SSVEP-based brain-computer interface (BCI) systems have received a lot of attention due to their relatively high Signal to Noise Ratio (SNR) and less training requirements. Most of the existing steady-state visual evoked potential (SSVEP) detection algorithms treat the prior probability of each alternative target being selected as equal. In this study, the prior probability distribution of alternative targets was introduced into the SSVEP recognition algorithm, and an asynchronous training-free SSVEP-BCI detection algorithm for non-equal prior probability scenarios was proposed. This algorithm is based on the Spatio-temporal equalization multi-window technique (STE-MW) and introduces the Maximum A Posteriori criterion (MAP), which makes full use of prior information to improve the performance of the asynchronous training-free BCI system. In addition, we proposed a mutual information-based performance evaluation metric called Mutual information rate (MIR) specifically for non-equal prior probability scenarios. This evaluation framework is designed to provide a more accurate estimation of the information transmission performance of BCI systems in such scenarios. A 10-target simulated vehicle control offline experiment involving 17 subjects showed that the proposed method improved the average MIR by 6.48%. Online free control experiments involving 12 subjects showed that the proposed method improved the average MIR by 14.93%, and significantly reduced the average instruction time. The proposed algorithm is more suitable for practical engineering application scenarios that are asynchronous and training-free; the extremely high accuracy is guaranteed while maintaining a low false alarm rate, which can be applied to asynchronous BCI systems that require high stability.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2024.3496727