Abstract
The purpose of this study is to apply a recently developed wavelet based de-noising filter to the analysis of human electroencephalogram (EEG) signals, and measure its performance. The data used contained subject EEG responses to two different stimuli using the `odd-ball' paradigm. Electrical signals measured at standard locations on the scalp were processed to detect and identify the Evoked Response Potentials (ERP's). First, electrical artifacts emitting from the eyes were identified and removed. Second, the mean signature for each type of response was extracted and used as a matched filter to define baseline detector performance for the noisy data. Third, a nonlinear filtering procedure based on the wavelet extrema representation was used to de-noise the signals. Overall detection rates for the de-noised signals were then compared to the baseline performance. It was found that while the filtered signals have significantly lower noise than the raw signals, detector performance remains comparable. We therefore conclude that all of the information that is important to matched filter detection is preserved by the filter. The implication is that the wavelet based filter eliminates much of the noise while retaining ERP's.
Original language | English (US) |
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Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
Publisher | Society of Photo-Optical Instrumentation Engineers |
Pages | 91-104 |
Number of pages | 14 |
Volume | 2303 |
ISBN (Print) | 0819416274 |
State | Published - Dec 1 1994 |
Externally published | Yes |
Event | Wavelet Applications in Signal and Image Processing II - San Diego, CA, USA Duration: Jul 27 1994 → Jul 29 1994 |
Other
Other | Wavelet Applications in Signal and Image Processing II |
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City | San Diego, CA, USA |
Period | 7/27/94 → 7/29/94 |
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering