Abstract
This paper is aimed at improving, through artifact rejection, the performance of neural network evoked potential (EP) classifiers designed to detect match/mismatch conditions. A cluster analysis approach is formulated to identify artifacts that occur in the signals used for training the neural network classifiers. The clustering based artifact detection algorithm uses a distance measure resulting from a nonlinear alignment procedure designed to optimally align EP signals. Match and mismatch EPs collected for network training are clustered and the identified artifact signals are excluded from the training set. Artifacts that occur during testing are also identified and rejected by including an additional output in the neural net classifier for the artifact class. Preliminary experiments conducted show significant improvements in classification accuracy when the proposed artifact rejection methods are incorporated in the training and testing phases of a neural network EP classifier.
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 | 676-686 |
Number of pages | 11 |
Volume | 2622 |
Edition | 2 |
ISBN (Print) | 0819419869, 9780819419866 |
DOIs | |
State | Published - 1995 |
Externally published | Yes |
Event | Optical Engineering Midwest'95. Part 2 (of 2) - Chicago, IL, USA Duration: May 18 1995 → May 19 1995 |
Other
Other | Optical Engineering Midwest'95. Part 2 (of 2) |
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City | Chicago, IL, USA |
Period | 5/18/95 → 5/19/95 |
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering