ABSTRACT
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.
ABSTRACT
We have brought forward a wavelet-based algorithm for electroencephalograph (EEG) signals--using scale dependent threshold based on median. In comparison with the universal threshold and Sure threshold, our proposed threshold, which is adaptive to the subband noise signals, preserves the noise free reconstruction property and takes lower risk than does the universal threshold; and our proposed threshold overcomes the drawback of Sure threshold. Evidently, the scale dependent threshold based on median is computationally simple and can obtain higher singal-to-noise ratio (SNR) it outperforms the universal threshold and Sure threshlold.