Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems
With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguard individual assets. The use of brain signals for authentication has gained substantial interest within the scientific community over the past decade....
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/7919 |
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| author | Adnan Elahi Khan Khalil Jesus Arturo Perez-Diaz Jose Antonio Cantoral-Ceballos Javier M. Antelis |
| author_facet | Adnan Elahi Khan Khalil Jesus Arturo Perez-Diaz Jose Antonio Cantoral-Ceballos Javier M. Antelis |
| author_sort | Adnan Elahi Khan Khalil |
| collection | DOAJ |
| description | With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguard individual assets. The use of brain signals for authentication has gained substantial interest within the scientific community over the past decade. Most previous efforts have focused on identifying distinctive information within electroencephalogram (EEG) recordings. In this study, an EEG-based user authentication scheme is presented, employing a multi-layer perceptron feedforward neural network (MLP FFNN). The scheme utilizes P300 potentials derived from EEG signals, focusing on the user’s intent to select specific characters. This approach involves two phases: user identification and user authentication. Both phases utilize EEG recordings of brain signals, data preprocessing, a database to store and manage these recordings for efficient retrieval and organization, and feature extraction using mutual information (MI) from selected EEG data segments, specifically targeting power spectral density (PSD) across five frequency bands. The user identification phase employs multi-class classifiers to predict the identity of a user from a set of enrolled users. The user authentication phase associates the predicted user identities with user labels using probability assessments, verifying the claimed identity as either genuine or an impostor. This scheme combines EEG data segments with user mapping, confidence calculations, and claimed user verification for robust authentication. It also accommodates new users by transforming EEG data into feature vectors without the need for retraining. The model extracts selected features to identify users and to classify the input based on these features to authenticate the user. The experiments show that the proposed scheme can achieve 97% accuracy in EEG-based user identification and authentication. |
| format | Article |
| id | doaj-art-ee7a600016d146ae92d4df30b1a08937 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-ee7a600016d146ae92d4df30b1a089372025-08-20T02:01:14ZengMDPI AGSensors1424-82202024-12-012424791910.3390/s24247919Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication SystemsAdnan Elahi Khan Khalil0Jesus Arturo Perez-Diaz1Jose Antonio Cantoral-Ceballos2Javier M. Antelis3School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64700, Nuevo Leon, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64700, Nuevo Leon, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64700, Nuevo Leon, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64700, Nuevo Leon, MexicoWith recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguard individual assets. The use of brain signals for authentication has gained substantial interest within the scientific community over the past decade. Most previous efforts have focused on identifying distinctive information within electroencephalogram (EEG) recordings. In this study, an EEG-based user authentication scheme is presented, employing a multi-layer perceptron feedforward neural network (MLP FFNN). The scheme utilizes P300 potentials derived from EEG signals, focusing on the user’s intent to select specific characters. This approach involves two phases: user identification and user authentication. Both phases utilize EEG recordings of brain signals, data preprocessing, a database to store and manage these recordings for efficient retrieval and organization, and feature extraction using mutual information (MI) from selected EEG data segments, specifically targeting power spectral density (PSD) across five frequency bands. The user identification phase employs multi-class classifiers to predict the identity of a user from a set of enrolled users. The user authentication phase associates the predicted user identities with user labels using probability assessments, verifying the claimed identity as either genuine or an impostor. This scheme combines EEG data segments with user mapping, confidence calculations, and claimed user verification for robust authentication. It also accommodates new users by transforming EEG data into feature vectors without the need for retraining. The model extracts selected features to identify users and to classify the input based on these features to authenticate the user. The experiments show that the proposed scheme can achieve 97% accuracy in EEG-based user identification and authentication.https://www.mdpi.com/1424-8220/24/24/7919multi-factor authenticationuser authenticationuser identificationelectroencephalogram (EEG)MLP neural networksmachine learning |
| spellingShingle | Adnan Elahi Khan Khalil Jesus Arturo Perez-Diaz Jose Antonio Cantoral-Ceballos Javier M. Antelis Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems Sensors multi-factor authentication user authentication user identification electroencephalogram (EEG) MLP neural networks machine learning |
| title | Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems |
| title_full | Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems |
| title_fullStr | Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems |
| title_full_unstemmed | Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems |
| title_short | Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems |
| title_sort | unlocking security for comprehensive electroencephalogram based user authentication systems |
| topic | multi-factor authentication user authentication user identification electroencephalogram (EEG) MLP neural networks machine learning |
| url | https://www.mdpi.com/1424-8220/24/24/7919 |
| work_keys_str_mv | AT adnanelahikhankhalil unlockingsecurityforcomprehensiveelectroencephalogrambaseduserauthenticationsystems AT jesusarturoperezdiaz unlockingsecurityforcomprehensiveelectroencephalogrambaseduserauthenticationsystems AT joseantoniocantoralceballos unlockingsecurityforcomprehensiveelectroencephalogrambaseduserauthenticationsystems AT javiermantelis unlockingsecurityforcomprehensiveelectroencephalogrambaseduserauthenticationsystems |