High accuracy EEG biometrics identification using ICA and AR model
Modern biometric identification methods combine interdisciplinary approaches to enhance person identification and classification accuracy. One popular technique for this purpose is Brain-Computer Interface (BCI).The signal so obtained from BCI will be further processed by the Autoregressive (AR) Mod...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universiti Utara Malaysia Press
2017
|
| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/24043/1/JICT%2016%202%20%202017%20354%E2%80%93373.pdf |
| _version_ | 1825805036085575680 |
|---|---|
| author | Kaewwit, Chesada Lursinsap, Chidchanok Sophatsathit, Peraphon |
| author_facet | Kaewwit, Chesada Lursinsap, Chidchanok Sophatsathit, Peraphon |
| author_sort | Kaewwit, Chesada |
| collection | UUM |
| description | Modern biometric identification methods combine interdisciplinary approaches to enhance person identification and classification accuracy. One popular technique for this purpose is Brain-Computer Interface (BCI).The signal so obtained from BCI will be further processed by the Autoregressive (AR) Model for feature extraction. Many researches in the area find that for more accurate results, the signal must be cleaned before extracting any useful feature information. This study proposes Independent Component Analysis (ICA), k-NN classifier, and AR as the combined techniques for electroencephalogram (EEG) biometrics to achieve the highest personal identification and classification accuracy. However, there is a classification gap between using the combined ICA with the AR model and AR model alone.Therefore, this study takes one step further by modifying the feature extraction of AR and comparing the outcome with the proposed approaches in lieu of prior researches. The experiment based on four relevant locations shows that the combined ICA and AR can achieve higher accuracy than the modified AR. More combinations of channels and subjects are required in future research to explore the significance of channel effects and to enhance the identification accuracy. |
| first_indexed | 2024-07-04T06:25:20Z |
| format | Article |
| id | uum-24043 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:25:20Z |
| publishDate | 2017 |
| publisher | Universiti Utara Malaysia Press |
| record_format | eprints |
| spelling | uum-240432018-04-29T01:42:26Z https://repo.uum.edu.my/id/eprint/24043/ High accuracy EEG biometrics identification using ICA and AR model Kaewwit, Chesada Lursinsap, Chidchanok Sophatsathit, Peraphon QA75 Electronic computers. Computer science Modern biometric identification methods combine interdisciplinary approaches to enhance person identification and classification accuracy. One popular technique for this purpose is Brain-Computer Interface (BCI).The signal so obtained from BCI will be further processed by the Autoregressive (AR) Model for feature extraction. Many researches in the area find that for more accurate results, the signal must be cleaned before extracting any useful feature information. This study proposes Independent Component Analysis (ICA), k-NN classifier, and AR as the combined techniques for electroencephalogram (EEG) biometrics to achieve the highest personal identification and classification accuracy. However, there is a classification gap between using the combined ICA with the AR model and AR model alone.Therefore, this study takes one step further by modifying the feature extraction of AR and comparing the outcome with the proposed approaches in lieu of prior researches. The experiment based on four relevant locations shows that the combined ICA and AR can achieve higher accuracy than the modified AR. More combinations of channels and subjects are required in future research to explore the significance of channel effects and to enhance the identification accuracy. Universiti Utara Malaysia Press 2017 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/24043/1/JICT%2016%202%20%202017%20354%E2%80%93373.pdf Kaewwit, Chesada and Lursinsap, Chidchanok and Sophatsathit, Peraphon (2017) High accuracy EEG biometrics identification using ICA and AR model. Journal of Information and Communication Technology, 16 (2). pp. 354-373. ISSN 2180-3862 http://jict.uum.edu.my/index.php/previous-issues/151-journal-of-information-and-communication-technology-jict-vol-16-no-2-december-2017#A6 |
| spellingShingle | QA75 Electronic computers. Computer science Kaewwit, Chesada Lursinsap, Chidchanok Sophatsathit, Peraphon High accuracy EEG biometrics identification using ICA and AR model |
| title | High accuracy EEG biometrics identification using ICA
and AR model |
| title_full | High accuracy EEG biometrics identification using ICA
and AR model |
| title_fullStr | High accuracy EEG biometrics identification using ICA
and AR model |
| title_full_unstemmed | High accuracy EEG biometrics identification using ICA
and AR model |
| title_short | High accuracy EEG biometrics identification using ICA
and AR model |
| title_sort | high accuracy eeg biometrics identification using ica and ar model |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/24043/1/JICT%2016%202%20%202017%20354%E2%80%93373.pdf |
| work_keys_str_mv | AT kaewwitchesada highaccuracyeegbiometricsidentificationusingicaandarmodel AT lursinsapchidchanok highaccuracyeegbiometricsidentificationusingicaandarmodel AT sophatsathitperaphon highaccuracyeegbiometricsidentificationusingicaandarmodel |