Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms
<p dir="ltr">Electroencephalography (EEG) based biometric systems are gaining attention for their anti-spoofing capability but lack accuracy due to signal variability at different psychological and physiological conditions. On the other hand, keystroke dynamics-based systems achieve...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , , |
| منشور في: |
2021
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| الموضوعات: | |
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| _version_ | 1864513560269291520 |
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| author | Arafat Rahman (8065562) |
| author2 | Muhammad E. H. Chowdhury (14150526) Amith Khandakar (14151981) Serkan Kiranyaz (3762058) Kh Shahriya Zaman (16891365) Mamun Bin Ibne Reaz (16875933) Mohammad Tariqul Islam (7854059) Maymouna Ezeddin (16891368) Muhammad Abdul Kadir (16869963) |
| author2_role | author author author author author author author author |
| author_facet | Arafat Rahman (8065562) Muhammad E. H. Chowdhury (14150526) Amith Khandakar (14151981) Serkan Kiranyaz (3762058) Kh Shahriya Zaman (16891365) Mamun Bin Ibne Reaz (16875933) Mohammad Tariqul Islam (7854059) Maymouna Ezeddin (16891368) Muhammad Abdul Kadir (16869963) |
| author_role | author |
| dc.creator.none.fl_str_mv | Arafat Rahman (8065562) Muhammad E. H. Chowdhury (14150526) Amith Khandakar (14151981) Serkan Kiranyaz (3762058) Kh Shahriya Zaman (16891365) Mamun Bin Ibne Reaz (16875933) Mohammad Tariqul Islam (7854059) Maymouna Ezeddin (16891368) Muhammad Abdul Kadir (16869963) |
| dc.date.none.fl_str_mv | 2021-06-28T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3092840 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Multimodal_EEG_and_Keystroke_Dynamics_Based_Biometric_System_Using_Machine_Learning_Algorithms/24042312 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Engineering Biomedical engineering Information and computing sciences Data management and data science Machine learning Electroencephalography Authentication Biometrics (access control) Feature extraction Machine learning Headphones Brain modeling Biometric system Electroencephalography (EEG) Keystroke dynamics Identification Multimodal system |
| dc.title.none.fl_str_mv | Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Electroencephalography (EEG) based biometric systems are gaining attention for their anti-spoofing capability but lack accuracy due to signal variability at different psychological and physiological conditions. On the other hand, keystroke dynamics-based systems achieve very high accuracy but have low anti-spoofing capability. To address these issues, a novel multimodal biometric system combining EEG and keystroke dynamics is proposed in this paper. A dataset was created by acquiring both keystroke dynamics and EEG signals simultaneously from 10 users. Each user participated in 500 trials at 10 different sessions (days) to replicate real-life signal variability. A machine learning classification pipeline is developed using multi-domain feature extraction (time, frequency, time-frequency), feature selection (Gini impurity), classifier design, and score level fusion. Different classifiers were trained, validated, and tested for two different classification experiments - personalized and generalized. For identification and authentication, 99.9% and 99.6% accuracies are achieved, respectively for the Random Forest classifier in 5 fold cross-validation. These results outperform the individual modalities with a significant margin (~5%). We also developed a binary template matching-based algorithm, which gives 93.64% accuracy 6X faster. The proposed method can be considered secure and reliable for any kind of biometric identification and authentication.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3092840" target="_blank">https://dx.doi.org/10.1109/access.2021.3092840</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0d5f04b8ff598862300fb66aad324aef |
| identifier_str_mv | 10.1109/access.2021.3092840 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24042312 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning AlgorithmsArafat Rahman (8065562)Muhammad E. H. Chowdhury (14150526)Amith Khandakar (14151981)Serkan Kiranyaz (3762058)Kh Shahriya Zaman (16891365)Mamun Bin Ibne Reaz (16875933)Mohammad Tariqul Islam (7854059)Maymouna Ezeddin (16891368)Muhammad Abdul Kadir (16869963)Biological sciencesBioinformatics and computational biologyEngineeringBiomedical engineeringInformation and computing sciencesData management and data scienceMachine learningElectroencephalographyAuthenticationBiometrics (access control)Feature extractionMachine learningHeadphonesBrain modelingBiometric systemElectroencephalography (EEG)Keystroke dynamicsIdentificationMultimodal system<p dir="ltr">Electroencephalography (EEG) based biometric systems are gaining attention for their anti-spoofing capability but lack accuracy due to signal variability at different psychological and physiological conditions. On the other hand, keystroke dynamics-based systems achieve very high accuracy but have low anti-spoofing capability. To address these issues, a novel multimodal biometric system combining EEG and keystroke dynamics is proposed in this paper. A dataset was created by acquiring both keystroke dynamics and EEG signals simultaneously from 10 users. Each user participated in 500 trials at 10 different sessions (days) to replicate real-life signal variability. A machine learning classification pipeline is developed using multi-domain feature extraction (time, frequency, time-frequency), feature selection (Gini impurity), classifier design, and score level fusion. Different classifiers were trained, validated, and tested for two different classification experiments - personalized and generalized. For identification and authentication, 99.9% and 99.6% accuracies are achieved, respectively for the Random Forest classifier in 5 fold cross-validation. These results outperform the individual modalities with a significant margin (~5%). We also developed a binary template matching-based algorithm, which gives 93.64% accuracy 6X faster. The proposed method can be considered secure and reliable for any kind of biometric identification and authentication.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3092840" target="_blank">https://dx.doi.org/10.1109/access.2021.3092840</a></p>2021-06-28T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3092840https://figshare.com/articles/journal_contribution/Multimodal_EEG_and_Keystroke_Dynamics_Based_Biometric_System_Using_Machine_Learning_Algorithms/24042312CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240423122021-06-28T00:00:00Z |
| spellingShingle | Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms Arafat Rahman (8065562) Biological sciences Bioinformatics and computational biology Engineering Biomedical engineering Information and computing sciences Data management and data science Machine learning Electroencephalography Authentication Biometrics (access control) Feature extraction Machine learning Headphones Brain modeling Biometric system Electroencephalography (EEG) Keystroke dynamics Identification Multimodal system |
| status_str | publishedVersion |
| title | Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms |
| title_full | Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms |
| title_fullStr | Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms |
| title_full_unstemmed | Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms |
| title_short | Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms |
| title_sort | Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms |
| topic | Biological sciences Bioinformatics and computational biology Engineering Biomedical engineering Information and computing sciences Data management and data science Machine learning Electroencephalography Authentication Biometrics (access control) Feature extraction Machine learning Headphones Brain modeling Biometric system Electroencephalography (EEG) Keystroke dynamics Identification Multimodal system |