Fine-Tuning EEG Channel Utilization for Emotionally Stimulated Biometric Authentication Article Swipe
Chetan Rakshe
,
T. Christy Bobby
,
Mohanavelu Kalathe
,
Vanteemar S. Sreeraj
,
Ganesan Venkatasubramanian
,
Deepesh Kumar
,
A. Amalin Prince
,
Jac Fredo Agastinose Ronickom
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3539502
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3539502
Biometric authentication relies on distinct biological traits of individuals to validate their identity, enhancing security measures. However, variations in an individual’s emotional state can impact the reliability of the biometric system. In this study, we propose a novel pipeline to evaluate electroencephalography (EEG)-based biometric system across different emotional states and optimize critical brain regions using machine learning algorithms. EEG signals from the DEAP dataset were classified into four emotional states: HAHV, HALV, LALV, and LAHV. We extracted a comprehensive set of statistical, time, frequency, entropy, fractal, spectral, and shape features from each channel. Machine learning classifiers, including Random Forest, Gradient Boosting, Extreme Gradient Boosting, LightGBM, CatBoost, and Bagging, were used for participant authentication. Our results revealed that the CatBoost classifier performed well across all stimuli with average accuracies of 84%, 85%, 86%, and 83% for HAHV, HALV, LALV, and LAHV, respectively. We found that features from channels FC1, Fz, C4 & Pz, and FC1 significantly contributed to EEG authentication on stimuli such as HAHV, HALV, LALV, and LAHV, respectively. Features such as skewness and the theta-to-alpha frequency band ratio consistently performed well across stimuli, demonstrating EEG signals’ potential for robust biometric authentication by addressing emotional variations.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2025.3539502
- OA Status
- gold
- Cited By
- 4
- References
- 73
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407168642Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2025.3539502Digital Object Identifier
- Title
-
Fine-Tuning EEG Channel Utilization for Emotionally Stimulated Biometric AuthenticationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
-
Chetan Rakshe, T. Christy Bobby, Mohanavelu Kalathe, Vanteemar S. Sreeraj, Ganesan Venkatasubramanian, Deepesh Kumar, A. Amalin Prince, Jac Fredo Agastinose RonickomList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2025.3539502Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/access.2025.3539502Direct OA link when available
- Concepts
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Biometrics, Computer science, Authentication (law), Electroencephalography, Channel (broadcasting), Speech recognition, Computer security, Psychology, Computer network, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
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73Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_topic.display_name | EEG and Brain-Computer Interfaces |
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