Title :
Efficient extraction of evoked potentials by combination of Wiener filtering and subspace methods
Author :
Cichocki, A. ; Gharieb, R.R. ; Hoya, T.
Author_Institution :
Lab. for Adv. Brain Signal Process., RIKEN, Saitama, Japan
Abstract :
A novel approach is proposed in order to reduce the number of sweeps (trials) required for the efficient extraction of the brain evoked potentials (EP). This approach is developed by combining both the Wiener filtering and the subspace methods. First, the signal subspace is estimated by applying the singular-value decomposition (SVD) to an enhanced version of the raw data obtained by Wiener filtering. Next, estimation of the EP data is achieved by orthonormal projection of the raw data onto the estimated signal subspace. Simulation results show that combination of both methods provides much better capability than each of them separately
Keywords :
Wiener filters; bioelectric potentials; brain; medical signal detection; parameter estimation; singular value decomposition; SVD; Wiener filtering; brain EP extraction; brain evoked potentials; orthonormal projection; signal subspace estimation; singular value decomposition; Brain modeling; Central nervous system; Centralized control; Data mining; Electric potential; Electronic mail; Noise reduction; Signal processing; Signal to noise ratio; Wiener filter;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
Conference_Location :
Salt Lake City, UT
Print_ISBN :
0-7803-7041-4
DOI :
10.1109/ICASSP.2001.940318