Fine-Tuning EEG Channel Utilization for Emotionally Stimulated Biometric Authentication
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 ev...
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2025-01-01
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| author | Chetan Rakshe Christy Bobby Thomas Mohanavelu Kalathe Vanteemar S. Sreeraj Ganesan Venkatasubramanian Deepesh Kumar A. Amalin Prince Jac Fredo Agastinose Ronickom |
| author_facet | Chetan Rakshe Christy Bobby Thomas Mohanavelu Kalathe Vanteemar S. Sreeraj Ganesan Venkatasubramanian Deepesh Kumar A. Amalin Prince Jac Fredo Agastinose Ronickom |
| author_sort | Chetan Rakshe |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fa0fe7a1a4ec438cad7013c19fe97eb4 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fa0fe7a1a4ec438cad7013c19fe97eb42025-08-20T02:15:32ZengIEEEIEEE Access2169-35362025-01-0113275372754910.1109/ACCESS.2025.353950210876141Fine-Tuning EEG Channel Utilization for Emotionally Stimulated Biometric AuthenticationChetan Rakshe0https://orcid.org/0000-0003-3354-4998Christy Bobby Thomas1https://orcid.org/0000-0002-7691-8120Mohanavelu Kalathe2https://orcid.org/0000-0002-0840-0346Vanteemar S. Sreeraj3Ganesan Venkatasubramanian4https://orcid.org/0000-0002-0949-898XDeepesh Kumar5https://orcid.org/0000-0002-5572-3391A. Amalin Prince6https://orcid.org/0000-0002-4471-9979Jac Fredo Agastinose Ronickom7https://orcid.org/0000-0001-5759-6632Computational Neuroscience and Biology Laboratory, School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, IndiaDepartment of Electronic and Communication Engineering, M. S. Ramaiah University of Applied Sciences, Bengaluru, IndiaDefence Bio-Engineering and Electro Medical Laboratory, DRDO, Ministry of Defence, Bangalore, IndiaNational Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, IndiaTranslational Psychiatry Laboratory, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, IndiaComputational Neuroscience and Biology Laboratory, School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, IndiaDepartment of Electrical and Electronics Engineering, BITS Pilani, K K Birla Goa Campus, Zuarinagar, Goa, IndiaComputational Neuroscience and Biology Laboratory, School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, IndiaBiometric 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.https://ieeexplore.ieee.org/document/10876141/Biometricelectroencephalographyemotionsbrain locationmachine learning |
| spellingShingle | Chetan Rakshe Christy Bobby Thomas Mohanavelu Kalathe Vanteemar S. Sreeraj Ganesan Venkatasubramanian Deepesh Kumar A. Amalin Prince Jac Fredo Agastinose Ronickom Fine-Tuning EEG Channel Utilization for Emotionally Stimulated Biometric Authentication IEEE Access Biometric electroencephalography emotions brain location machine learning |
| title | Fine-Tuning EEG Channel Utilization for Emotionally Stimulated Biometric Authentication |
| title_full | Fine-Tuning EEG Channel Utilization for Emotionally Stimulated Biometric Authentication |
| title_fullStr | Fine-Tuning EEG Channel Utilization for Emotionally Stimulated Biometric Authentication |
| title_full_unstemmed | Fine-Tuning EEG Channel Utilization for Emotionally Stimulated Biometric Authentication |
| title_short | Fine-Tuning EEG Channel Utilization for Emotionally Stimulated Biometric Authentication |
| title_sort | fine tuning eeg channel utilization for emotionally stimulated biometric authentication |
| topic | Biometric electroencephalography emotions brain location machine learning |
| url | https://ieeexplore.ieee.org/document/10876141/ |
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