Model-based noise reduction for single trial evoked potentials

Author

Flexer, Arthur ; Bauer, Herbert ; Lamm, Claus ; Dorffner, Georg

Author_Institution

Austrian Res. Inst. for Artificial Intelligence, Vienna, Austria

fYear

2001

fDate

2001

Firstpage

499

Lastpage

508

Abstract

Two model-based techniques, Gaussian mixture models with integrated noise component and principal component analysis, are applied to noise reduction for single trial evoked potentials which are buried in noise up to five times stronger than the signal. An empirical study using artificial data is presented and results are compared to the standard technique of averaging

Keywords

bioelectric potentials; electroencephalography; principal component analysis; Gaussian mixture models; artificial data; averaging; integrated noise component; model-based noise reduction; principal component analysis; single trial evoked potentials; Anthropometry; Brain; Delay; Electrodes; Gaussian noise; Noise reduction; Principal component analysis; Psychology; Signal analysis; Signal to noise ratio;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing XI, 2001. Proceedings of the 2001 IEEE Signal Processing Society Workshop

Conference_Location

North Falmouth, MA

ISSN

1089-3555

Print_ISBN

0-7803-7196-8

Type

conf

DOI

10.1109/NNSP.2001.943154

Filename

943154

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3373880