Evoked potentials(EPs) are nonstationary and have characteristics that vary throughout stimulus trails. With the development of signal processing, new techniques focus on using fewer trails to extract the evoked potentials and to achieve single-trial extraction of evoked potentials finally. Therefore, fewer trials or single-trial estimation of evoked potentials has become the hotspot in the field of biomedical signal processing. In this article, signal processing methods of fast extraction of evoked potentials developed in recent years are reviewed and the approaches based on adaptive filter, wavelet transform, neural network and independent component analysis are discussed.

ObjectiveTo efficiently detect somsatosensory evoked potential (SEP) using field programmable gate array (FPGA) real-time system, fixed-point algorithm based adaptive noise canceller (ANC) was designed to improve signal to noise ratio (SNR). MethodsWith the optimization of important parameters that influence the performance of fixed-point algorithm ANC, the performance was compared to that of floating-point algorithm ANC which was isolated from the effect of quantization error. Results In the simulation study, the outputs of fixed-point-based ANC showed a little higher distortion from real SEP signals than that of floating-point algorithm ANC. In the optimal selection of μ value, fixed-point algorithm ANC could get as good results as floating-point algorithm. Conclusion With appropriate parameter values, fixed-point algorithm ANC is able to improve SNR of SEP as well as that of fixed-point algorithm ANC.

ObjectiveTo design multi-adaptive filter based on radial basis function (MAF-RBF) for efficiently extracting somatosensory evoked potential (SEP) in real-time SEP monitoring.MethodsWith the optimization of important parameters that influence the performance of radial basis function neural network,the performance of extracting SEP was compared to that of a multi-adaptive filter (MAF),which developed from the combination of well-developed adaptive noise canceller and adaptive signal enhancer.ResultsIn this simulation study,the outputs of MAF-RBF showed a similar waveform with SEP template signals,and a smoother waveform than the.output of MAF.ConclusionWith appropriate parameter values,MAF-RBFNN is able to extract the latency and amplitude of SEP from the extremely noisy background rapidly and reliably without averaging.