EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm
The electroencephalogram (EEG) introduced a massive potential for user identification. Several studies have shown that EEG provides unique features in addition to typical strength for spoofing attacks. EEG provides a graphic recording of the brain’s electrical activity that electrodes can capture on...
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| Format: | Article |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/6/2092 |
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| author | Zaid Abdi Alkareem Alyasseri Osama Ahmad Alomari João P. Papa Mohammed Azmi Al-Betar Karrar Hameed Abdulkareem Mazin Abed Mohammed Seifedine Kadry Orawit Thinnukool Pattaraporn Khuwuthyakorn |
| author_facet | Zaid Abdi Alkareem Alyasseri Osama Ahmad Alomari João P. Papa Mohammed Azmi Al-Betar Karrar Hameed Abdulkareem Mazin Abed Mohammed Seifedine Kadry Orawit Thinnukool Pattaraporn Khuwuthyakorn |
| author_sort | Zaid Abdi Alkareem Alyasseri |
| collection | DOAJ |
| description | The electroencephalogram (EEG) introduced a massive potential for user identification. Several studies have shown that EEG provides unique features in addition to typical strength for spoofing attacks. EEG provides a graphic recording of the brain’s electrical activity that electrodes can capture on the scalp at different places. However, selecting which electrodes should be used is a challenging task. Such a subject is formulated as an electrode selection task that is tackled by optimization methods. In this work, a new approach to select the most representative electrodes is introduced. The proposed algorithm is a hybrid version of the Flower Pollination Algorithm and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-Hill Climbing optimizer called FPA<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-hc. The performance of the FPA<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-hc algorithm is evaluated using a standard EEG motor imagery dataset. The experimental results show that the FPA<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-hc can utilize less than half of the electrode numbers, achieving more accurate results than seven other methods. |
| first_indexed | 2024-03-09T12:43:09Z |
| format | Article |
| id | doaj.art-b7f05b6c681d430a8a3793a016a59938 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:43:09Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b7f05b6c681d430a8a3793a016a599382023-11-30T22:16:07ZengMDPI AGSensors1424-82202022-03-01226209210.3390/s22062092EEG Channel Selection Based User Identification via Improved Flower Pollination AlgorithmZaid Abdi Alkareem Alyasseri0Osama Ahmad Alomari1João P. Papa2Mohammed Azmi Al-Betar3Karrar Hameed Abdulkareem4Mazin Abed Mohammed5Seifedine Kadry6Orawit Thinnukool7Pattaraporn Khuwuthyakorn8ECE Department, Faculty of Engineering, University of Kufa, Najaf 54001, IraqMLALP Research Group, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesDepartment of Computing, UNESP—São Paulo State University, Bauru 19060-560, BrazilArtificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 20550, United Arab EmiratesCollege of Agriculture, Al-Muthanna University, Samawah 66001, IraqCollege of Computer Science and Information Technology, University of Anbar, Ramadi 31001, IraqDepartment of Applied Data Science, Norrof University College, 4608 Kristiansand, NorwayCollege of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, ThailandCollege of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, ThailandThe electroencephalogram (EEG) introduced a massive potential for user identification. Several studies have shown that EEG provides unique features in addition to typical strength for spoofing attacks. EEG provides a graphic recording of the brain’s electrical activity that electrodes can capture on the scalp at different places. However, selecting which electrodes should be used is a challenging task. Such a subject is formulated as an electrode selection task that is tackled by optimization methods. In this work, a new approach to select the most representative electrodes is introduced. The proposed algorithm is a hybrid version of the Flower Pollination Algorithm and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-Hill Climbing optimizer called FPA<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-hc. The performance of the FPA<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-hc algorithm is evaluated using a standard EEG motor imagery dataset. The experimental results show that the FPA<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula>-hc can utilize less than half of the electrode numbers, achieving more accurate results than seven other methods.https://www.mdpi.com/1424-8220/22/6/2092EEGbiometric<i>β</i>-hill climbingflower pollination algorithmfeature selectionauto-repressive |
| spellingShingle | Zaid Abdi Alkareem Alyasseri Osama Ahmad Alomari João P. Papa Mohammed Azmi Al-Betar Karrar Hameed Abdulkareem Mazin Abed Mohammed Seifedine Kadry Orawit Thinnukool Pattaraporn Khuwuthyakorn EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm Sensors EEG biometric <i>β</i>-hill climbing flower pollination algorithm feature selection auto-repressive |
| title | EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm |
| title_full | EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm |
| title_fullStr | EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm |
| title_full_unstemmed | EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm |
| title_short | EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm |
| title_sort | eeg channel selection based user identification via improved flower pollination algorithm |
| topic | EEG biometric <i>β</i>-hill climbing flower pollination algorithm feature selection auto-repressive |
| url | https://www.mdpi.com/1424-8220/22/6/2092 |
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