Fast Extraction of Somatosensory Evoked Potential Using RLS Adaptive Filter Algorithms

Author

Ren, Zhaoli ; Zou, Yuexian ; Zhang, Zhiguo ; Hu, Yong

Author_Institution

Adv. Digital Signal Process. Lab., Peking Univ., Shenzhen, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

4

Abstract

This paper evaluates the efficacy of the recursive least squares (RLS) in adaptive noise canceller (RLS-ANC) for fast extraction of somatosensory evoked potentials (SEPs). The RLSANC method was verified by simulation of electroencephalography (EEG) and Gaussian noise contaminated SEP signals at different signal-to-noise ratios (SNRs). RLS was found to converge faster than the least mean squares (LMS) algorithm in ANC, i.e. SEP extraction by RLS-ANC required fewer trials than LMS-ANC. Experimental results showed that RLS-ANC with less than 50 trials could provide similar performance in SEP extraction to those extracted by the conventional ensemble averaging with 500 trials even at SNR of 20 dB.

Keywords

electroencephalography; filtering theory; least squares approximations; mechanoception; medical signal processing; neurophysiology; recursive estimation; signal denoising; EEG SEP signals; Gaussian noise contaminated SEP signals; RLS adaptive filter algorithms; RLSANC method; adaptive noise canceller; electroencephalography; somatosensory evoked potential fast extraction; the recursive least squares; Adaptive filters; Brain modeling; Electroencephalography; Filtering algorithms; Least squares approximation; Monitoring; Noise cancellation; Resonance light scattering; Signal processing algorithms; Signal to noise ratio;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing, 2009. CISP '09. 2nd International Congress on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4129-7

Electronic_ISBN

978-1-4244-4131-0

Type

conf

DOI

10.1109/CISP.2009.5304009

Filename

5304009