A review of researches on decoding algorithms of steady-state visual evoked potentials / 生物医学工程学杂志

Brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) have become one of the major paradigms in BCI research due to their high signal-to-noise ratio and short training time required by users. Fast and accurate decoding of SSVEP features is a crucial step in SSVEP-BCI research. However, the current researches lack a systematic overview of SSVEP decoding algorithms and analyses of the connections and differences between them, so it is difficult for researchers to choose the optimum algorithm under different situations. To address this problem, this paper focuses on the progress of SSVEP decoding algorithms in recent years and divides them into two categories-trained and non-trained-based on whether training data are needed. This paper also explains the fundamental theories and application scopes of decoding algorithms such as canonical correlation analysis (CCA), task-related component analysis (TRCA) and the extended algorithms, concludes the commonly used strategies for processing decoding algorithms, and discusses the challenges and opportunities in this field in the end.

Decoding algorithm of neural spike signals in brain-computer interface / 国际生物医学工程杂志

The core problem of the brain-computer interface (BCI) based on neural signal is estimating neural firing rate from a spike train and then using neural population decoding algorithm to decode movement trajectory.In this artical, we review the theoretical basis of both classic and current firing rate estimations and compare the advantages and drawbacks of these methods. At the same time we also review the decoding algorithm which using neural firing rate to decode movement trajectory in brain- computer interface: population vector algorithm, linear filter and kalman filter. At last, some results applying these estimators of firing rate to decode arm movement in BCI are introduced. The results show apparently different performance of the different firing rate estimators, while minimal differences are observed in the actual application of BCI.