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: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
Springer India
2017
|
| 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 |
| _version_ | 1796992096924598272 |
|---|---|
| author | Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Cumming, Paul Marizan, Mubin |
| author_facet | Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Cumming, Paul Marizan, Mubin |
| author_sort | Asrul, Adam |
| collection | UMP |
| description | 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 event-related applications. Therefore, we aimed to develop a general procedure for eye event-related applications based on feature weight learning (FWL), through the use of a neural network with random weights (NNRW) as the classifier. The FWL is performed using a particle swarm optimization algorithm, applied to the well-studied Dumpala, Acir, Liu and Dingle peak detection models, where the associated features are considered as inputs to the NNRW with and without FWL. The combination of all the associated features from the four models is also considered, as a comprehensive model for validation purposes. Real EEG data recorded from two channels of 20 healthy volunteers were used to perform the model simulations. The data set consisted of 40 peaks arising in the frontal eye field in association with a change of horizontal eye gaze direction. It was found that the NNRW in conjunction with FWL has better performance than NNRW alone for all four peak detection models, of which the Dingle model gave the highest performance, with 74% accuracy. |
| first_indexed | 2024-03-06T12:15:02Z |
| format | Article |
| id | UMPir17480 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T12:15:02Z |
| publishDate | 2017 |
| publisher | Springer India |
| record_format | dspace |
| spelling | UMPir174802018-02-07T07:43:38Z http://umpir.ump.edu.my/id/eprint/17480/ Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Cumming, Paul Marizan, Mubin TK Electrical engineering. Electronics Nuclear engineering 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 event-related applications. Therefore, we aimed to develop a general procedure for eye event-related applications based on feature weight learning (FWL), through the use of a neural network with random weights (NNRW) as the classifier. The FWL is performed using a particle swarm optimization algorithm, applied to the well-studied Dumpala, Acir, Liu and Dingle peak detection models, where the associated features are considered as inputs to the NNRW with and without FWL. The combination of all the associated features from the four models is also considered, as a comprehensive model for validation purposes. Real EEG data recorded from two channels of 20 healthy volunteers were used to perform the model simulations. The data set consisted of 40 peaks arising in the frontal eye field in association with a change of horizontal eye gaze direction. It was found that the NNRW in conjunction with FWL has better performance than NNRW alone for all four peak detection models, of which the Dingle model gave the highest performance, with 74% accuracy. Springer India 2017 Article PeerReviewed application/pdf en 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 application/pdf en 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 Asrul, Adam and Zuwairie, Ibrahim and Norrima, Mokhtar and Mohd Ibrahim, Shapiai and Cumming, Paul and Marizan, Mubin (2017) Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications. Sadhana, 42 (5). pp. 1-13. ISSN 0256-2499(print); 0973-7677(online). (Published) https://doi.org/10.1007/s12046-017-0633-9 DOI: 10.1007/s12046-017-0633-9 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Cumming, Paul Marizan, Mubin Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications |
| title | Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications |
| title_full | Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications |
| title_fullStr | Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications |
| title_full_unstemmed | Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications |
| title_short | Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications |
| title_sort | improving eeg signal peak detection using feature weight learning of a neural network with random weights for eye event related applications |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | 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 |
| work_keys_str_mv | AT asruladam improvingeegsignalpeakdetectionusingfeatureweightlearningofaneuralnetworkwithrandomweightsforeyeeventrelatedapplications AT zuwairieibrahim improvingeegsignalpeakdetectionusingfeatureweightlearningofaneuralnetworkwithrandomweightsforeyeeventrelatedapplications AT norrimamokhtar improvingeegsignalpeakdetectionusingfeatureweightlearningofaneuralnetworkwithrandomweightsforeyeeventrelatedapplications AT mohdibrahimshapiai improvingeegsignalpeakdetectionusingfeatureweightlearningofaneuralnetworkwithrandomweightsforeyeeventrelatedapplications AT cummingpaul improvingeegsignalpeakdetectionusingfeatureweightlearningofaneuralnetworkwithrandomweightsforeyeeventrelatedapplications AT marizanmubin improvingeegsignalpeakdetectionusingfeatureweightlearningofaneuralnetworkwithrandomweightsforeyeeventrelatedapplications |