Abstract
A feedforward neural network with one hidden layer is applied to the problem of brainstem auditory evoked potential classification. Network performances were tested separately both on subject-dependent samples (drawn from the same subjects from which the training set was derived) and on subject-independent samples (drawn from subjects from which no data were included in the training set), and compared. The results indicate that, while increasing the training set size improves performance, human-selected training sets give better results than randomly selected sets. Different encoding schemes used for representing the signal yield varying rates of correct recognition. Although the networks were overly complex and trained in the memorization mode, they show some feature extracting and generalization capabilities.
| Original language | English |
|---|---|
| Title of host publication | Unknown Host Publication Title |
| Place of Publication | Piscataway, NJ, United States |
| Publisher | Publ by IEEE |
| Pages | 1266-1271 |
| Number of pages | 6 |
| ISBN (Print) | 0780302273 |
| State | Published - Dec 1 1991 |
| Externally published | Yes |
| Event | 1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 - Singapore, Singapore Duration: Nov 18 1991 → Nov 21 1991 |
Other
| Other | 1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 |
|---|---|
| City | Singapore, Singapore |
| Period | 11/18/91 → 11/21/91 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Brainstem auditory evoked potential classification by backpropagation networks. / Alpsan, D.; Ozdamar, Ozcan.
Unknown Host Publication Title. Piscataway, NJ, United States : Publ by IEEE, 1991. p. 1266-1271.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Brainstem auditory evoked potential classification by backpropagation networks
AU - Alpsan, D.
AU - Ozdamar, Ozcan
PY - 1991/12/1
Y1 - 1991/12/1
N2 - A feedforward neural network with one hidden layer is applied to the problem of brainstem auditory evoked potential classification. Network performances were tested separately both on subject-dependent samples (drawn from the same subjects from which the training set was derived) and on subject-independent samples (drawn from subjects from which no data were included in the training set), and compared. The results indicate that, while increasing the training set size improves performance, human-selected training sets give better results than randomly selected sets. Different encoding schemes used for representing the signal yield varying rates of correct recognition. Although the networks were overly complex and trained in the memorization mode, they show some feature extracting and generalization capabilities.
AB - A feedforward neural network with one hidden layer is applied to the problem of brainstem auditory evoked potential classification. Network performances were tested separately both on subject-dependent samples (drawn from the same subjects from which the training set was derived) and on subject-independent samples (drawn from subjects from which no data were included in the training set), and compared. The results indicate that, while increasing the training set size improves performance, human-selected training sets give better results than randomly selected sets. Different encoding schemes used for representing the signal yield varying rates of correct recognition. Although the networks were overly complex and trained in the memorization mode, they show some feature extracting and generalization capabilities.
UR - http://www.scopus.com/inward/record.url?scp=0026283245&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0026283245&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0026283245
SN - 0780302273
SP - 1266
EP - 1271
BT - Unknown Host Publication Title
PB - Publ by IEEE
CY - Piscataway, NJ, United States
ER -