An idle state-detecting method based on transient visual evoked potentials for an asynchronous ERP-based BCI

• Published in: Journal of neuroscience methods 2020-05, Vol.337, p.108670-108670, Article 108670
• Main Authors: Gong, Minghong, Xu, Guizhi, Li, Mengfan, Lin, Fang
• Format: Article
• Language: English
• Subjects: Asynchronous; Brain-computer interface; Event-related potentials; Probability-based FLDA; Transient visual evoked potential
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2020.108670

•An odd-ball paradigm could evoke transient visual evoked potentials (TSVEPs) simultaneously with ERPs.•The study combines the frequency features of TSVEP and the time features of ERP to build an asynchronous brain-computer interface system.•A probability-based fisher linear discriminant analysis (P-FLDA) is proposed to detect the brian states based on the TSVEP and ERP.•The designed asynchronous system achieves higher accuracy and information transfer rate than the traditional ERP BCI. An asynchronous brain-computer interface (BCI) allows subject to freely switch between the working state and the idle state, improving the subject’s comfort. However, using only the event-related potential (ERP) to detect these two states is difficult because of the small amplitude of the ERP. Our previous study finds that an odd-ball paradigm could evoke transient visual evoked potentials (TSVEPs) simultaneously with ERPs. This study adopts the TSVEP and the ERP to detect the idle state in the design of an asynchronous TSVEP-ERP-based BCI (T-E BCI). The T-E BCI extracts time and frequency features from brain signals and uses a novel probability-based fisher linear discriminant analysis (P-FLDA) to combine the classification results of the ERP and the TSVEP. Ten subjects perform visual speller and video watching experiments, and their brain signals are measured under the working and idle states. The main results show that the T-E BCI achieves a higher accuracy than the ERP-based BCI when judging the subject’s intentions and the two states. The P-FLDA performs better than the FLDA in combining the classification results. The study demonstrates that adding the TSVEP can substantially reduce the number of wrongly detected trials. The T-E BCI provides a new way of designing an asynchronous BCI without adding any additional visual stimuli, which makes the BCI more practical.