Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results
Most studies on EEG-based biometry recognition report results based on signal databases, with a limited number of recorded EEG sessions using the same single EEG recording for both training and testing a proposed model. However, the EEG signal is highly vulnerable to interferences, electrode placeme...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/4/2057 |
| _version_ | 1797618268673933312 |
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| author | Renata Plucińska Konrad Jędrzejewski Urszula Malinowska Jacek Rogala |
| author_facet | Renata Plucińska Konrad Jędrzejewski Urszula Malinowska Jacek Rogala |
| author_sort | Renata Plucińska |
| collection | DOAJ |
| description | Most studies on EEG-based biometry recognition report results based on signal databases, with a limited number of recorded EEG sessions using the same single EEG recording for both training and testing a proposed model. However, the EEG signal is highly vulnerable to interferences, electrode placement, and temporary conditions, which can lead to overestimated assessments of the considered methods. Our study examined how different numbers of distinct recording sessions used as training sessions would affect EEG-based verification. We analyzed the original data from 29 participants with 20 distinct recorded sessions each, as well as 23 additional impostors with only one session each. We applied raw coefficients of power spectral density estimate, and the coefficients of power spectral density estimate converted to the decibel scale, as the input to a shallow neural network. Our study showed that the variance introduced by multiple recording sessions affects sensitivity. We also showed that increasing the number of sessions above eight did not improve the results under our conditions. For 15 training sessions, the achieved accuracy was 96.7 ± 4.2%, and for eight training sessions and 12 test sessions, it was 94.9 ± 4.6%. For 15 training sessions, the rate of successful impostor attacks over all attack attempts was 3.1 ± 2.2%, but this number was not significantly different from using six recording sessions for training. Our findings indicate the need to include data from multiple recording sessions in EEG-based recognition for training, and that increasing the number of test sessions did not significantly affect the obtained results. Although the presented results are for the resting-state, they may serve as a baseline for other paradigms. |
| first_indexed | 2024-03-11T08:10:39Z |
| format | Article |
| id | doaj.art-3a3260b4d9f94697b0eef45fd0d8be76 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:10:39Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3a3260b4d9f94697b0eef45fd0d8be762023-11-16T23:09:44ZengMDPI AGSensors1424-82202023-02-01234205710.3390/s23042057Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification ResultsRenata Plucińska0Konrad Jędrzejewski1Urszula Malinowska2Jacek Rogala3Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, PolandInstitute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, PolandInstitute of Experimental Physics, Faculty of Physics, University of Warsaw, 02-093 Warsaw, PolandInstitute of Experimental Physics, Faculty of Physics, University of Warsaw, 02-093 Warsaw, PolandMost studies on EEG-based biometry recognition report results based on signal databases, with a limited number of recorded EEG sessions using the same single EEG recording for both training and testing a proposed model. However, the EEG signal is highly vulnerable to interferences, electrode placement, and temporary conditions, which can lead to overestimated assessments of the considered methods. Our study examined how different numbers of distinct recording sessions used as training sessions would affect EEG-based verification. We analyzed the original data from 29 participants with 20 distinct recorded sessions each, as well as 23 additional impostors with only one session each. We applied raw coefficients of power spectral density estimate, and the coefficients of power spectral density estimate converted to the decibel scale, as the input to a shallow neural network. Our study showed that the variance introduced by multiple recording sessions affects sensitivity. We also showed that increasing the number of sessions above eight did not improve the results under our conditions. For 15 training sessions, the achieved accuracy was 96.7 ± 4.2%, and for eight training sessions and 12 test sessions, it was 94.9 ± 4.6%. For 15 training sessions, the rate of successful impostor attacks over all attack attempts was 3.1 ± 2.2%, but this number was not significantly different from using six recording sessions for training. Our findings indicate the need to include data from multiple recording sessions in EEG-based recognition for training, and that increasing the number of test sessions did not significantly affect the obtained results. Although the presented results are for the resting-state, they may serve as a baseline for other paradigms.https://www.mdpi.com/1424-8220/23/4/2057biometryEEGelectroencephalographyneural networkPSDverification |
| spellingShingle | Renata Plucińska Konrad Jędrzejewski Urszula Malinowska Jacek Rogala Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results Sensors biometry EEG electroencephalography neural network PSD verification |
| title | Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results |
| title_full | Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results |
| title_fullStr | Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results |
| title_full_unstemmed | Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results |
| title_short | Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results |
| title_sort | leveraging multiple distinct eeg training sessions for improvement of spectral based biometric verification results |
| topic | biometry EEG electroencephalography neural network PSD verification |
| url | https://www.mdpi.com/1424-8220/23/4/2057 |
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