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AuthorRahman A.
AuthorChowdhury M.E.H.
AuthorKhandakar A.
AuthorKiranyaz, Mustafa Serkan
AuthorZaman K.S.
AuthorReaz M.B.I.
AuthorIslam M.T.
AuthorEzeddin M.
AuthorKadir M.A.
Available date2022-04-26T12:31:19Z
Publication Date2021
Publication NameIEEE Access
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ACCESS.2021.3092840
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85112145604&doi=10.1109%2fACCESS.2021.3092840&partnerID=40&md5=58ea2f01e76c7d265493fc868c120fe6
URIhttp://hdl.handle.net/10576/30596
AbstractElectroencephalography (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.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectAuthentication
Biomedical signal processing
Biometrics
Decision trees
Dynamics
Electroencephalography
Electrophysiology
Feature extraction
Machine learning
Template matching
Turing machines
Binary template matching
Biometric identifications
Machine learning classification
Multi-domain features
Multimodal biometric systems
Physiological condition
Random forest classifier
Signal variability
Learning algorithms
TitleMultimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms
TypeArticle
Pagination94625-94643
Volume Number9
dc.accessType Abstract Only


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