Yousefi, Fares and Kolivand, Hoshang (2023) A robust brain pattern for brain-based authentication methods using deep breath. Computers & Security, 135. ISSN 1872-6208
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Abstract
Security authentication involves the process of verifying a person’s identity. Authentication technology has played a crucial role in data security for many years. However, existing typical biometric authentication technologies exhibit limitations related to usability, time efficiency, and notably, the long-term viability of the method. Recent technological advancements have led to the development of specific devices capable of reproducing human biometrics due to their visibility and tactile nature. Consequently, there is a demand for a new biometric method to address the limitations of current authentication systems. Human brain signals have been utilized in various Brain-Computer Interface (BCI) applications. Nevertheless, this approach also faces challenges related to usability, time efficiency, and most importantly, the stability of the method over time. Studies reveal that the stability of brain patterns poses a significant challenge in EEG-based authentication techniques. Stability refers to the capacity to withstand changes or disruptions, while permanency implies a lasting and unchanging state. Notably, stability can be temporary and subject to fluctuations, whereas permanency suggests a more enduring condition. Research demonstrates that utilizing alpha brainwaves is a superior option for authentication compared to other brainwave types. Many brain states lack stability in different situations. Interestingly, deep breathing can enhance alpha waves irrespective of the brain’s current state. To explore the potential of utilizing deep breathing as a security pattern for authentication purposes, an experiment was conducted to investigate its effects on brain activity and its role in enhancing alpha brainwaves. By focusing on bolstering the permanency of brain patterns, our aim is to address the challenges associated with stability in EEG-based authentication techniques. The experimental results exhibited a high success rate of 91 % and 90 % for Support Vector Machine and Neural Network classifiers, respectively. These results suggest that deep breathing not only enhances permanency but could also serve as a suitable option for a brainwave-based authentication method.
| Item Type: | Article |
|---|---|
| Additional Information and Comments: | © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | BCI; Brain-based authentication; Brain-computer interface; Brain state stability; EEG signal |
| Faculty / Department: | Faculty of Human and Digital Sciences > Mathematics and Computer Science |
| Depositing User: | Fares Yousefi |
| Date Deposited: | 16 Apr 2024 11:25 |
| Last Modified: | 16 Apr 2024 11:25 |
| URI: | https://hira.hope.ac.uk/id/eprint/4186 |
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