Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications
The optimization of peak detection algorithms for electroencephalogram (EEG) signal analysis is an ongoing project; previously existing algorithms have been used with different models to detect EEG peaks in various applications. However, none of the existing techniques perform adequately in eye even...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Springer India
2017
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/17480/1/Improving%20EEG%20signal%20peak%20detection%20using%20feature%20weight%20learning%20of%20a%20neural%20network%20with%20random%20weights%20for%20eye%20event-related%20applications.pdf http://umpir.ump.edu.my/id/eprint/17480/3/Improving%20EEG%20signal%20peak%20detection%20using%20feature%20weight%20learning%20of%20a%20neural%20network%20with%20random%20weights%20for%20eye%20event-related%20applications%201.pdf |