Brain–Computer Interface for EEG-Based Authentication: Advancements and Practical Implications
Authentication is a critical component of digital security, and traditional methods often encounter significant vulnerabilities and limitations. This study addresses the emerging field of EEG-based authentication systems, highlighting their theoretical advancements and practical applicability. We co...
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| Vydáno v: | Sensors (Basel, Switzerland) Ročník 25; číslo 16; s. 4946 |
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10.08.2025
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| ISSN: | 1424-8220, 1424-8220 |
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| Abstract | Authentication is a critical component of digital security, and traditional methods often encounter significant vulnerabilities and limitations. This study addresses the emerging field of EEG-based authentication systems, highlighting their theoretical advancements and practical applicability. We conducted a systematic review of the existing literature, followed by an experimental evaluation to assess the feasibility, limitations, and scalability of these systems in real-world scenarios. Data were collected from nine subjects using various approaches. Our results indicate that the CNN model achieved the highest accuracy of 99%, while Random Forest (RF) and Gradient Boosting (GB) classifiers also demonstrated strong performance with 94% and 93%, respectively. In contrast, classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) displayed significantly lower effectiveness, underscoring their limitations in capturing the complexities of EEG data. The findings suggest that EEG-based authentication systems have significant potential to enhance security measures, offering a promising alternative to traditional methods and paving the way for more robust and user-friendly authentication solutions. |
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| AbstractList | Authentication is a critical component of digital security, and traditional methods often encounter significant vulnerabilities and limitations. This study addresses the emerging field of EEG-based authentication systems, highlighting their theoretical advancements and practical applicability. We conducted a systematic review of the existing literature, followed by an experimental evaluation to assess the feasibility, limitations, and scalability of these systems in real-world scenarios. Data were collected from nine subjects using various approaches. Our results indicate that the CNN model achieved the highest accuracy of 99%, while Random Forest (RF) and Gradient Boosting (GB) classifiers also demonstrated strong performance with 94% and 93%, respectively. In contrast, classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) displayed significantly lower effectiveness, underscoring their limitations in capturing the complexities of EEG data. The findings suggest that EEG-based authentication systems have significant potential to enhance security measures, offering a promising alternative to traditional methods and paving the way for more robust and user-friendly authentication solutions. Authentication is a critical component of digital security, and traditional methods often encounter significant vulnerabilities and limitations. This study addresses the emerging field of EEG-based authentication systems, highlighting their theoretical advancements and practical applicability. We conducted a systematic review of the existing literature, followed by an experimental evaluation to assess the feasibility, limitations, and scalability of these systems in real-world scenarios. Data were collected from nine subjects using various approaches. Our results indicate that the CNN model achieved the highest accuracy of 99%, while Random Forest (RF) and Gradient Boosting (GB) classifiers also demonstrated strong performance with 94% and 93%, respectively. In contrast, classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) displayed significantly lower effectiveness, underscoring their limitations in capturing the complexities of EEG data. The findings suggest that EEG-based authentication systems have significant potential to enhance security measures, offering a promising alternative to traditional methods and paving the way for more robust and user-friendly authentication solutions.Authentication is a critical component of digital security, and traditional methods often encounter significant vulnerabilities and limitations. This study addresses the emerging field of EEG-based authentication systems, highlighting their theoretical advancements and practical applicability. We conducted a systematic review of the existing literature, followed by an experimental evaluation to assess the feasibility, limitations, and scalability of these systems in real-world scenarios. Data were collected from nine subjects using various approaches. Our results indicate that the CNN model achieved the highest accuracy of 99%, while Random Forest (RF) and Gradient Boosting (GB) classifiers also demonstrated strong performance with 94% and 93%, respectively. In contrast, classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) displayed significantly lower effectiveness, underscoring their limitations in capturing the complexities of EEG data. The findings suggest that EEG-based authentication systems have significant potential to enhance security measures, offering a promising alternative to traditional methods and paving the way for more robust and user-friendly authentication solutions. |
| Audience | Academic |
| Author | Aljumah, Hessah Aldayel, Mashael Alahaideb, Lamia Al-Nafjan, Abeer |
| AuthorAffiliation | 1 Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia 2 Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia |
| AuthorAffiliation_xml | – name: 1 Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia – name: 2 Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia |
| Author_xml | – sequence: 1 givenname: Lamia surname: Alahaideb fullname: Alahaideb, Lamia – sequence: 2 givenname: Abeer orcidid: 0000-0003-4186-9805 surname: Al-Nafjan fullname: Al-Nafjan, Abeer – sequence: 3 givenname: Hessah surname: Aljumah fullname: Aljumah, Hessah – sequence: 4 givenname: Mashael orcidid: 0000-0001-6054-4534 surname: Aldayel fullname: Aldayel, Mashael |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40871810$$D View this record in MEDLINE/PubMed |
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| Keywords | brain–computer interface (BCI) electroencephalography (EEG) event-related potentials (ERP) convolutional neural networks (CNN) authentication |
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| SubjectTerms | Accuracy Adaptive technology Algorithms Analysis authentication Biometric identification Biometrics Brain research Brain-Computer Interfaces brain–computer interface (BCI) Classification Computer Security convolutional neural networks (CNN) Data encryption chips Datasets Electroencephalography electroencephalography (EEG) Electroencephalography - methods event-related potentials (ERP) Fourier transforms Humans Literature reviews Machine learning Neural Networks, Computer Neurophysiology Rapid serial visual presentation Safety and security measures Security management Security systems Signal processing Support Vector Machine Support vector machines Systematic review Systems and data security software |
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| Title | Brain–Computer Interface for EEG-Based Authentication: Advancements and Practical Implications |
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