Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications

• Main Authors: Asrul, Adam, Zuwairie, Ibrahim, Norrima, Mokhtar, Mohd Ibrahim, Shapiai, Cumming, Paul, Marizan, Mubin
• Format: Article
• Language: English; English
• Published: Springer India 2017
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• 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; http://umpir.ump.edu.my/id/eprint/17480/; https://doi.org/10.1007/s12046-017-0633-9

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...