Robust-NeuroBiometrics: sistema robusto de identificación biométrica basado en señales de electroencefalograma (EEG)

En este trabajo se adaptaron tres modelos de aprendizaje profundo del estado del arte en la clasificación de señales de electroencefalograma (EEG) al aprendizaje multi-tarea de dos tareas relevantes en la biometría: identificación de usuarios y detección de intrusos. Los resultados evidenciaron un m...

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Autores:: González Estrada, Gabriel Francisco

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2024

Institución:: Universidad de los Andes

Repositorio:: Séneca: repositorio Uniandes

Idioma:: spa

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dc.title.none.fl_str_mv	Robust-NeuroBiometrics: sistema robusto de identificación biométrica basado en señales de electroencefalograma (EEG)
title	Robust-NeuroBiometrics: sistema robusto de identificación biométrica basado en señales de electroencefalograma (EEG)
spellingShingle	Robust-NeuroBiometrics: sistema robusto de identificación biométrica basado en señales de electroencefalograma (EEG) Biometría Electroencefalograma Ciberseguridad Interfaz cerebro-máquina Aprendizaje multi-tarea Deep learning Transformers Ingeniería
title_short	Robust-NeuroBiometrics: sistema robusto de identificación biométrica basado en señales de electroencefalograma (EEG)
title_full	Robust-NeuroBiometrics: sistema robusto de identificación biométrica basado en señales de electroencefalograma (EEG)
title_fullStr	Robust-NeuroBiometrics: sistema robusto de identificación biométrica basado en señales de electroencefalograma (EEG)
title_full_unstemmed	Robust-NeuroBiometrics: sistema robusto de identificación biométrica basado en señales de electroencefalograma (EEG)
title_sort	Robust-NeuroBiometrics: sistema robusto de identificación biométrica basado en señales de electroencefalograma (EEG)
dc.creator.fl_str_mv	González Estrada, Gabriel Francisco
dc.contributor.advisor.none.fl_str_mv	Lozano Martínez, Fernando Enrique Arbeláez Escalante, Pablo Andrés
dc.contributor.author.none.fl_str_mv	González Estrada, Gabriel Francisco
dc.contributor.jury.none.fl_str_mv	Osma Cruz, Johann Faccelo
dc.subject.keyword.spa.fl_str_mv	Biometría Electroencefalograma Ciberseguridad Interfaz cerebro-máquina Aprendizaje multi-tarea
topic	Biometría Electroencefalograma Ciberseguridad Interfaz cerebro-máquina Aprendizaje multi-tarea Deep learning Transformers Ingeniería
dc.subject.keyword.eng.fl_str_mv	Deep learning Transformers
dc.subject.themes.spa.fl_str_mv	Ingeniería
description	En este trabajo se adaptaron tres modelos de aprendizaje profundo del estado del arte en la clasificación de señales de electroencefalograma (EEG) al aprendizaje multi-tarea de dos tareas relevantes en la biometría: identificación de usuarios y detección de intrusos. Los resultados evidenciaron un mejor desempeño de los modelos multi-tarea frente a los modelos diseñados para una sola tarea, cuando se realizó simultáneamente identificación de usuarios y detección de intrusos.
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-07-09T14:35:20Z
dc.date.available.none.fl_str_mv	2024-07-09T14:35:20Z
dc.date.issued.none.fl_str_mv	2024-07-08
dc.type.none.fl_str_mv	Trabajo de grado - Pregrado
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dc.relation.references.none.fl_str_mv	Shaymaa Adnan Abdulrahman and Bilal Alhayani. A comprehensive survey on the biometric systems based on physiological and behavioural characteristics. Materials Today: Proceedings, 80:2642–2646, 2023. 1, 2 Pablo Arnau-González, Stamos Katsigiannis, Miguel Arevalillo-Herráez, and Naeem Ramzan. Bed: A new data set for eeg-based biometrics. IEEE Internet of Things Journal, 8(15):12219–12230, 2021. 2, 4 Nicholas A Badcock, Petroula Mousikou, Yatin Mahajan, Peter De Lissa, Johnson Thie, and Genevieve McArthur. Validation of the emotiv epoc® eeg gaming system for measuring research quality auditory erps. PeerJ, 1:e38, 2013. 4 Kaliraman Bhawna, Priyanka, and Manoj Duhan. Electroencephalogram based biometric system: A review. In Gurdeep Singh Hura, Ashutosh Kumar Singh, and Lau Siong Hoe, editors, Advances in Communication and Computational Technology, pages 57–77, Singapore, 2021. Springer Nature Singapore. 1, 2, 3 Amir Jalaly Bidgoly, Hamed Jalaly Bidgoly, and Zeynab Arezoumand. A survey on methods and challenges in eeg based authentication. Computers & Security, 93:101788, 2020. 1, 2, 3 Lukas Biewald. Experiment tracking with weights and biases, 2020. Software available from wandb.com. 9 Hui-Ling Chan, Po-Chih Kuo, Chia-Yi Cheng, and Yong-Sheng Chen. Challenges and future perspectives on electroencephalogram-based biometrics in person recognition. Frontiers in neuroinformatics, 12:66, 2018. 2, 3 Sanghyun Choo, Hoonseok Park, Sangyeon Kim, Donghyun Park, Jae-Yoon Jung, Sangwon Lee, and Chang S Nam. Effectiveness of multi-task Deep learning framework for eeg-based emotion and context recognition. Expert Systems with Applications, 227:120348, 2023. 4 Wenhui Cui,Woojae Jeong, Philipp Th¨olke, Takfarinas Medani, Karim Jerbi, Anand A Joshi, and Richard M Leahy. Neuro-gpt: Developing a foundation model for eeg. arXiv preprint arXiv:2311.03764, 2023. 3 Alicia Curth and Mihaela Van der Schaar. On inductive biases for heterogeneous treatment effect estimation. Advances in Neural Information Processing Systems, 34:15883–15894, 2021. 4 Shaveta Dargan and Munish Kumar. A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. Expert Systems with Applications, 143:113114, 2020. 1 Christos A Fidas and Dimitrios Lyras. A review of eegbased user authentication: trends and future research directions. IEEE Access, 2023. 1, 2, 3 Ary L. Goldberger, Luis A. N. Amaral, Leon Glass, Jeffrey M. Hausdorff, Plamen Ch. Ivanov, Roger G. Mark, Joseph E. Mietus, George B. Moody, Chung- Kang Peng, and H. Eugene Stanley. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation, 101(23):E215–20, 2000. 4 Valer Jurcak, Daisuke Tsuzuki, and Ippeita Dan. 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. Neuroimage, 34(4):1600–1611, 2007. 2, 8 Aleksandra Kawala-Sterniuk, Natalia Browarska, Amir Al-Bakri, Mariusz Pelc, Jaroslaw Zygarlicki, Michaela Sidikova, Radek Martinek, and Edward Jacek Gorzelanczyk. Summary of over fifty years with brain-computer interfaces—a review. Brain Sciences, 11(1):43, 2021. 2 Demetres Kostas, Stephane Aroca-Ouellette, and Frank Rudzicz. Bendr: using transformers and a contrastive self-supervised learning task to learn from massive amounts of eeg data. Frontiers in Human Neuroscience, 15:653659, 2021. 3, 6, 7 Demetres Kostas and Frank Rudzicz. Dn3: An opensource python library for large-scale raw neurophysiology data assimilation for more flexible and standardized deep learning. bioRxiv, pages 2020–12, 2020. 8 Emanuele Maiorana. Deep learning for eeg-based biometric recognition. Neurocomputing, 410:374–386, 2020. 2, 3, 10, 12 Shervin Minaee, Amirali Abdolrashidi, Hang Su, Mohammed Bennamoun, and David Zhang. Biometrics recognition using deep learning: A survey. Artificial Intelligence Review, 56(8):8647–8695, 2023. 1 Luis Alfredo Moctezuma and Marta Molinas. Multiobjective optimization for eeg channel selection and accurate intruder detection in an eeg-based subject identification system. Scientific reports, 10(1):5850, 2020. 2, 3 Iyad Obeid and Joseph Picone. The temple university hospital eeg data corpus. Frontiers in neuroscience, 10:195498, 2016. 6 G. Schalk, D.J. McFarland, T. Hinterberger, N. Birbaumer, and J.R. Wolpaw. Bci2000: a general-purpose brain-computer interface (bci) system. IEEE Transactions on Biomedical Engineering, 51(6):1034–1043, 2004. 4 Kashif Shaheed, Aihua Mao, Imran Qureshi, Munish Kumar, Qaisar Abbas, Inam Ullah, and Xingming Zhang. A systematic review on physiologicalbased biometric recognition systems: current and future trends. Archives of Computational Methods in Engineering, pages 1–44, 2021. 1 Tarik Bin Shams, Md Sakir Hossain, Md Firoz Mahmud, Md Shahariar Tehjib, Zahid Hossain, and Md Ileas Pramanik. Eeg-based biometric authentication using machine learning: A comprehensive survey. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 20(2):225–241, 2022. 3 Yonghao Song, Qingqing Zheng, Bingchuan Liu, and Xiaorong Gao. Eeg conformer: Convolutional transformer for eeg decoding and visualization. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31:710–719, 2022. 3, 5, 6 Shiliang Sun. Multitask learning for eeg-based biometrics. In 2008 19th international conference on pattern recognition, pages 1–4. IEEE, 2008. 4 Yingnan Sun, Frank P.-W. Lo, and Benny Lo. Eegbased user identification system using 1d-convolutional long short-term memory neural networks. Expert Systems with Applications, 125:259–267, 2019. 3, 5, 9 Mohd Noorulfakhri Yaacob, Syed Zulkarnain Syed Idrus, Wan Nor Ashiqin Wan Ali, Wan Azani Mustafa, Mohd Aminudin Jamlos, and Mohd Helmy Abd Wahab. Decision making process in keystroke dynamics. In Journal of Physics: Conference Series, volume 1529, page 022087. IOP Publishing, 2020. 9 Shuai Zhang, Lei Sun, Xiuqing Mao, Cuiyun Hu, and Peiyuan Liu. Review on eeg-based authentication technology. Computational Intelligence and Neuroscience, 2021(1):5229576, 2021. 3 Yu Zhang and Qiang Yang. A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering, 34(12):5586–5609, 2021. 3, 4
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spelling	Lozano Martínez, Fernando Enriquevirtual::18715-1Arbeláez Escalante, Pablo Andrésvirtual::18717-1González Estrada, Gabriel FranciscoOsma Cruz, Johann Faccelovirtual::18718-12024-07-09T14:35:20Z2024-07-09T14:35:20Z2024-07-08https://hdl.handle.net/1992/74494instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/En este trabajo se adaptaron tres modelos de aprendizaje profundo del estado del arte en la clasificación de señales de electroencefalograma (EEG) al aprendizaje multi-tarea de dos tareas relevantes en la biometría: identificación de usuarios y detección de intrusos. Los resultados evidenciaron un mejor desempeño de los modelos multi-tarea frente a los modelos diseñados para una sola tarea, cuando se realizó simultáneamente identificación de usuarios y detección de intrusos.La identificación de usuarios basada en señales de EEG surge como una solución innovadora en sistemas de alta seguridad. Hasta el momento, no se ha planteado la posibilidad de realizar la identificación de usuarios y la detección de intrusos de forma paralela, pues los métodos convencionales primero realizan una verificación de identidad y luego realizan la identificación. En este trabajo se propone utilizar la técnica de aprendizaje multi-tarea en tres modelos de aprendizaje profundo del estado del arte en clasificación de señales de EEG, para aprender simultáneamente las tareas de identificación de usuarios y detección de intrusos. Además, se evidencia que esta técnica permite obtener representaciones más robustas de las señales, mejorando el desempeño en al menos una de las tareas frente a los enfoques que utilizan una sola tarea. Adicionalmente, se evalúa el desempeño de los modelos en dos bases de datos que utilizan distintos protocolos de adquisición, donde una cuenta únicamente con datos de una sesión de grabación y otra de tres sesiones, adquiridas con una semana de diferencia. Esto se hizo para determinar la robustez de las características extraídas por los modelos frente a cambios fisiológicos relacionados con la edad y cambios psicológicos de los sujetos. Se evidenció que el desempeño de los modelos disminuyó al utilizar la base de datos que tuvo múltiples sesiones.Pregrado13 páginasapplication/pdfspaUniversidad de los AndesIngeniería ElectrónicaFacultad de IngenieríaDepartamento de Ingeniería Eléctrica y ElectrónicaAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Robust-NeuroBiometrics: sistema robusto de identificación biométrica basado en señales de electroencefalograma (EEG)Trabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPBiometríaElectroencefalogramaCiberseguridadInterfaz cerebro-máquinaAprendizaje multi-tareaDeep learningTransformersIngenieríaShaymaa Adnan Abdulrahman and Bilal Alhayani. A comprehensive survey on the biometric systems based on physiological and behavioural characteristics. Materials Today: Proceedings, 80:2642–2646, 2023. 1, 2Pablo Arnau-González, Stamos Katsigiannis, Miguel Arevalillo-Herráez, and Naeem Ramzan. Bed: A new data set for eeg-based biometrics. IEEE Internet of Things Journal, 8(15):12219–12230, 2021. 2, 4Nicholas A Badcock, Petroula Mousikou, Yatin Mahajan, Peter De Lissa, Johnson Thie, and Genevieve McArthur. Validation of the emotiv epoc® eeg gaming system for measuring research quality auditory erps. PeerJ, 1:e38, 2013. 4Kaliraman Bhawna, Priyanka, and Manoj Duhan. Electroencephalogram based biometric system: A review. In Gurdeep Singh Hura, Ashutosh Kumar Singh, and Lau Siong Hoe, editors, Advances in Communication and Computational Technology, pages 57–77, Singapore, 2021. Springer Nature Singapore. 1, 2, 3Amir Jalaly Bidgoly, Hamed Jalaly Bidgoly, and Zeynab Arezoumand. A survey on methods and challenges in eeg based authentication. Computers & Security, 93:101788, 2020. 1, 2, 3Lukas Biewald. Experiment tracking with weights and biases, 2020. Software available from wandb.com. 9Hui-Ling Chan, Po-Chih Kuo, Chia-Yi Cheng, and Yong-Sheng Chen. Challenges and future perspectives on electroencephalogram-based biometrics in person recognition. Frontiers in neuroinformatics, 12:66, 2018. 2, 3Sanghyun Choo, Hoonseok Park, Sangyeon Kim, Donghyun Park, Jae-Yoon Jung, Sangwon Lee, and Chang S Nam. Effectiveness of multi-task Deep learning framework for eeg-based emotion and context recognition. Expert Systems with Applications, 227:120348, 2023. 4Wenhui Cui,Woojae Jeong, Philipp Th¨olke, Takfarinas Medani, Karim Jerbi, Anand A Joshi, and Richard M Leahy. Neuro-gpt: Developing a foundation model for eeg. arXiv preprint arXiv:2311.03764, 2023. 3Alicia Curth and Mihaela Van der Schaar. On inductive biases for heterogeneous treatment effect estimation. Advances in Neural Information Processing Systems, 34:15883–15894, 2021. 4Shaveta Dargan and Munish Kumar. A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. Expert Systems with Applications, 143:113114, 2020. 1Christos A Fidas and Dimitrios Lyras. A review of eegbased user authentication: trends and future research directions. IEEE Access, 2023. 1, 2, 3Ary L. Goldberger, Luis A. N. Amaral, Leon Glass, Jeffrey M. Hausdorff, Plamen Ch. Ivanov, Roger G. Mark, Joseph E. Mietus, George B. Moody, Chung- Kang Peng, and H. Eugene Stanley. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation, 101(23):E215–20, 2000. 4Valer Jurcak, Daisuke Tsuzuki, and Ippeita Dan. 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. Neuroimage, 34(4):1600–1611, 2007. 2, 8Aleksandra Kawala-Sterniuk, Natalia Browarska, Amir Al-Bakri, Mariusz Pelc, Jaroslaw Zygarlicki, Michaela Sidikova, Radek Martinek, and Edward Jacek Gorzelanczyk. Summary of over fifty years with brain-computer interfaces—a review. Brain Sciences, 11(1):43, 2021. 2Demetres Kostas, Stephane Aroca-Ouellette, and Frank Rudzicz. Bendr: using transformers and a contrastive self-supervised learning task to learn from massive amounts of eeg data. Frontiers in Human Neuroscience, 15:653659, 2021. 3, 6, 7Demetres Kostas and Frank Rudzicz. Dn3: An opensource python library for large-scale raw neurophysiology data assimilation for more flexible and standardized deep learning. bioRxiv, pages 2020–12, 2020. 8Emanuele Maiorana. Deep learning for eeg-based biometric recognition. Neurocomputing, 410:374–386, 2020. 2, 3, 10, 12Shervin Minaee, Amirali Abdolrashidi, Hang Su, Mohammed Bennamoun, and David Zhang. Biometrics recognition using deep learning: A survey. Artificial Intelligence Review, 56(8):8647–8695, 2023. 1Luis Alfredo Moctezuma and Marta Molinas. Multiobjective optimization for eeg channel selection and accurate intruder detection in an eeg-based subject identification system. Scientific reports, 10(1):5850, 2020. 2, 3Iyad Obeid and Joseph Picone. The temple university hospital eeg data corpus. Frontiers in neuroscience, 10:195498, 2016. 6G. Schalk, D.J. McFarland, T. Hinterberger, N. Birbaumer, and J.R. Wolpaw. Bci2000: a general-purpose brain-computer interface (bci) system. IEEE Transactions on Biomedical Engineering, 51(6):1034–1043, 2004. 4Kashif Shaheed, Aihua Mao, Imran Qureshi, Munish Kumar, Qaisar Abbas, Inam Ullah, and Xingming Zhang. A systematic review on physiologicalbased biometric recognition systems: current and future trends. Archives of Computational Methods in Engineering, pages 1–44, 2021. 1Tarik Bin Shams, Md Sakir Hossain, Md Firoz Mahmud, Md Shahariar Tehjib, Zahid Hossain, and Md Ileas Pramanik. Eeg-based biometric authentication using machine learning: A comprehensive survey. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 20(2):225–241, 2022. 3Yonghao Song, Qingqing Zheng, Bingchuan Liu, and Xiaorong Gao. Eeg conformer: Convolutional transformer for eeg decoding and visualization. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31:710–719, 2022. 3, 5, 6Shiliang Sun. Multitask learning for eeg-based biometrics. In 2008 19th international conference on pattern recognition, pages 1–4. IEEE, 2008. 4Yingnan Sun, Frank P.-W. Lo, and Benny Lo. Eegbased user identification system using 1d-convolutional long short-term memory neural networks. Expert Systems with Applications, 125:259–267, 2019. 3, 5, 9Mohd Noorulfakhri Yaacob, Syed Zulkarnain Syed Idrus, Wan Nor Ashiqin Wan Ali, Wan Azani Mustafa, Mohd Aminudin Jamlos, and Mohd Helmy Abd Wahab. Decision making process in keystroke dynamics. In Journal of Physics: Conference Series, volume 1529, page 022087. IOP Publishing, 2020. 9Shuai Zhang, Lei Sun, Xiuqing Mao, Cuiyun Hu, and Peiyuan Liu. Review on eeg-based authentication technology. Computational Intelligence and Neuroscience, 2021(1):5229576, 2021. 3Yu Zhang and Qiang Yang. A survey on multi-task learning. 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Robust-NeuroBiometrics: sistema robusto de identificación biométrica basado en señales de electroencefalograma (EEG)

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