ARCHIVES

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6. F. Brocal, “Brain-computer interfaces in safety and security fields: Risks and applications,” Safety Science, vol. 160, p. 106051, Apr.
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8. L. Hernández-Álvarez, E. Barbierato, S. Caputo, L. Mucchi, and L. Hernández Encinas, “EEG Authentication System Based on One-
and Multi-Class Machine Learning Classifiers,” Sensors, vol. 23, no. 1, p. 186, Dec. 2022, doi: https://doi.org/10.3390/s23010186.
9. C. Rudin, “Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead,”
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11. Sotiris Pelekis et al., “Adversarial machine learning: a review of methods, tools, and critical industry sectors,” Artificial Intelligence
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12. E. T. Martínez Beltrán, M. Quiles Pérez, S. López Bernal, A. Huertas Celdrán, and G. Martínez Pérez, “Noise-based cyberattacks
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13. U. Nawaz, M. Anees-ur-Rahaman, and Z. Saeed, “A review of neuro-symbolic AI integrating reasoning and learning for advanced
cognitive systems,” Intelligent Systems with Applications, vol. 26, p. 200541, May 2025, doi:
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14. M. F. Mridha, S. C. Das, M. M. Kabir, A. A. Lima, Md. R. Islam, and Y. Watanobe, “Brain-Computer Interface: Advancement and
Challenges,” Sensors, vol. 21, no. 17, p. 5746, Aug. 2021, doi: https://doi.org/10.3390/s21175746.
15. H. Yadav and S. Maini, “Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities,”
Multimedia Tools and Applications, May 2023, doi: https://doi.org/10.1007/s11042-023-15653-x.
16. A. Chaddad, Y. Wu, Reem Kateb, and A. Bouridane, “Electroencephalography Signal Processing: A Comprehensive Review and
Analysis of Methods and Techniques,” Sensors, vol. 23, no. 14, pp. 6434–6434, Jul. 2023, doi: https://doi.org/10.3390/s23146434.
17. M. Saeidi et al., “Neural Decoding of EEG Signals with Machine Learning: A Systematic Review,” Brain Sciences, vol. 11, no. 11,
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18. M. W. Mathis, A. P. Rotondo, E. F. Chang, A. S. Tolias, and A. Mathis, “Decoding the brain: From neural representations to
mechanistic models,” Cell, vol. 187, no. 21, pp. 5814–5832, Oct. 2024, doi: https://doi.org/10.1016/j.cell.2024.08.051.
19. A. Wajnerman Paz, “Is Your Neural Data Part of Your Mind? Exploring the Conceptual Basis of Mental Privacy,” Minds and
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20. B. S. McEwen and P. J. Gianaros, “Central role of the brain in stress and adaptation: Links to socioeconomic status, health, and
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21. A. Wells and A. B. Usman, “Privacy and biometrics for smart healthcare systems: attacks, and techniques,” Information Security
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22. M. Goncalves and D. Dangelo, “Regulating the Mind: Neuromarketing, Neural Data and Stakeholder Trust Under California’s
CCPA,” Administrative Sciences, vol. 15, no. 10, p. 386, Sep. 2025, doi: https://doi.org/10.3390/admsci15100386.
23. S. Burwell, M. Sample, and E. Racine, “Ethical aspects of brain computer interfaces: a scoping review,” BMC Medical Ethics, vol.
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24. Y. Huang et al., “A wearable group-synchronized EEG system for multi-subject brain–computer interfaces,” Frontiers in
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25. L. E. Matzen, Z. N. Gastelum, B. C. Howell, K. M. Divis, and M. C. Stites, “Effects of machine learning errors on human decision-
making: manipulations of model accuracy, error types, and error importance,” Cognitive Research Principles and Implications, vol.
9, no. 1, Aug. 2024, doi: https://doi.org/10.1186/s41235-024-00586-2.
26. S. Ajrawi, R. Rao, and M. Sarkar, “Cybersecurity in Brain-Computer Interfaces: RFID-based design-theoretical framework,”
Informatics in Medicine Unlocked, vol. 22, p. 100489, Jan. 2021, doi: https://doi.org/10.1016/j.imu.2020.100489.
27. Adedotun Lawrence Omotade, “Neurotechnology and Human-Machine Interfaces: Securing Brain-Computer Interfaces (BCIs)
Against Hacking,” World Journal of Advanced Research and Reviews, vol. 27, no. 1, pp. 2761–2771, Jul. 2025, doi:
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28. R. Hasan and R. Hasan, “Threat model and security analysis of video conferencing systems as a communication paradigm during the
COVID-19 pandemic,” pp. 181–199, Jan. 2022, doi: https://doi.org/10.1016/b978-0-323-90054-6.00009-x.
29. X.-Y. Liu et al., “Recent applications of EEG-based brain-computer-interface in the medical field,” Military Medical Research, vol.
12, no. 1, Mar. 2025, doi: https://doi.org/10.1186/s40779-025-00598-z.
30. T. Merk, V. Peterson, R. Köhler, S. Haufe, R. M. Richardson, and W.-J. Neumann, “Machine learning based brain signal decoding
for intelligent adaptive deep brain stimulation,” Experimental Neurology, vol. 351, p. 113993, May 2022, doi:
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31. S. Huang et al., “U.S. public perceptions of the sensitivity of brain data,” Journal of Law and the Biosciences, vol. 11, no. 1, Jan.
2024, doi: https://doi.org/10.1093/jlb/lsad032.
32. P. Magee, Marcello Ienca, and N. Farahany, “Beyond neural data: Cognitive biometrics and mental privacy,” Neuron, vol. 112, n o.
18, pp. 3017–3028, Sep. 2024, doi: https://doi.org/10.1016/j.neuron.2024.09.004.
33. J. Henriksen-Bulmer and S. Jeary, “Re-identification attacks—A systematic literature review,” International Journal of Information
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34. M. Attia et al., “Cognitive, emotional, physical, and behavioral stress-related symptoms and coping strategies among universitystudents during the third wave of COVID-19 pandemic,” Frontiers in Psychiatry, vol. 13, no. 933981, 2022, doi:
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35. G. Mezzina, V. F. Annese, and D. De Venuto, “A Cybersecure P300-Based Brain-to-Computer Interface against Noise-Based and
Fake P300 Cyberattacks,” Sensors, vol. 21, no. 24, p. 8280, Dec. 2021, doi: https://doi.org/10.3390/s21248280.
36. J. Samuel, T. K. Murugan, L. Govindaraj, M. Balaji, V. SenthilKumar, and S. Sundararajan, “Adversarial robust EEG-based brain–
computer interfaces using a hierarchical convolutional neural network,” Scientific Reports, vol. 16, no. 1, Jan. 2026, doi:
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37. Z. Wang, Q. Kang, X. Zhang, and Q. Hu, “Defense Strategies Toward Model Poisoning Attacks in Federated Learning: A Survey,”
2022 IEEE Wireless Communications and Networking Conference (WCNC), Apr. 2022, doi:
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38. M. L. Hernandez-Jaimes, A. Martinez-Cruz, K. A. Ramírez-Gutiérrez, and C. Feregrino-Uribe, “Artificial intelligence for IoMT
security: A review of intrusion detection systems, attacks, datasets and Cloud–Fog–Edge architectures,” Internet of Things, vol. 23,
p. 100887, Oct. 2023, doi: https://doi.org/10.1016/j.iot.2023.100887.
39. Lamia Alahaideb, Abeer Al-Nafjan, Hessah Aljumah, and Mashael Aldayel, “Brain–Computer Interface for EEG-Based
Authentication: Advancements and Practical Implications,” Sensors, vol. 25, no. 16, pp. 4946–4946, Aug. 2025, doi:
https://doi.org/10.3390/s25164946.
40. K. M. van Hespen, J. J. M. Zwanenburg, J. W. Dankbaar, M. I. Geerlings, J. Hendrikse, and H. J. Kuijf, “An anomaly detection
approach to identify chronic brain infarcts on MRI,” Scientific Reports, vol. 11, no. 1, p. 7714, Apr. 2021, doi:
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41. Francisco Javier Ramírez-Arias et al., “Evaluation of Machine Learning Algorithms for Classification of EEG Signals,” Technologies
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42. I. H. Sarker, H. Janicke, Mohamed Amine Ferrag, and Alsharif Abuadbba, “Multi-aspect rule-based AI: Methods, taxonomy,
challenges and directions toward automation, intelligence and transparent cybersecurity modeling for critical infrastructures,” Internet
of Things, vol. 25, pp. 101110–101110, Feb. 2024, doi: https://doi.org/10.1016/j.iot.2024.101110.
43. P. Artiemjew, L. Rudikova, and O. Myslivets, “About Rule-Based Systems: Single Database Queries for Decision Making,” Future
Internet, vol. 12, no. 12, p. 212, Nov. 2020, doi: https://doi.org/10.3390/fi12120212.
44. C. Beisbart, “In Which Ways Is Machine Learning Opaque?,” Synthese Library, pp. 3–24, Dec. 2025, doi:
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45. P. Radanliev, “AI Ethics: Integrating Transparency, Fairness, and Privacy in AI Development,” Applied Artificial Intelligence, vol.
39, no. 1, Feb. 2025, doi: https://doi.org/10.1080/08839514.2025.2463722.
46. L. Meziane, W. Abbaoui, S. Abdellaoui, B. El Bhiri, and S. Ziti, “Narrative Review on Symbolic Approaches for Explainable
Artificial Intelligence: Foundations, Challenges, and Perspectives,” ICATH 2025, p. 39, Oct. 2025, doi:
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47. Prashani Jayasingha Arachchige, Bogdan Iancu, and Johan Lilius, “Welcome To Zscaler Directory Authentication,” Ieee.org, 2026 .
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48. J. Zhang, B. Chen, L. Zhang, X. Ke, and H. Ding, “Neural, symbolic and neural-symbolic reasoning on knowledge graphs,” AI Open,
vol. 2, pp. 14–35, 2021, doi: https://doi.org/10.1016/j.aiopen.2021.03.001.
49. O. Bougzime, C. Cruz, J.-C. André, K. Zhou, H. Jerry Qi, and F. Demoly, “Neuro-symbolic artificial intelligence in accelerated
design for 4D printing: Status, challenges, and perspectives,” Materials & Design, p. 113737, Feb. 2025, doi:
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50. B. Liang, Y. Wang, and C. Tong, “AI Reasoning in Deep Learning Era: From Symbolic AI to Neural–Symbolic AI,” Mathematics,
vol. 13, no. 11, pp. 1707–1707, May 2025, doi: https://doi.org/10.3390/math13111707.
51. O. Haggag, A. Pedace, S. Pan, and J. Grundy, “An analysis of privacy regulations and user concerns of finance mobile applications,”
Information and Software Technology, vol. 184, p. 107756, Apr. 2025, doi: https://doi.org/10.1016/j.infsof.2025.107756.
52. Z. Gao et al., “Complex networks and deep learning for EEG signal analysis,” Cognitive Neurodynamics, vol. 15, no. 3, pp. 369–
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53. M. Han, Y. Kuang, Y. Zhao, Y. Zhang, J. Xiong, and Y. Zhang, “Psychological stress on human mobility and emergency evacuation
in disaster response,” Transportation Research Part D: Transport and Environment, vol. 148, p. 105022, Oct. 2025, doi:
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54. J. Liu and Y. Tang, “Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence
Distance,” Entropy, vol. 23, no. 7, pp. 820–820, Jun. 2021, doi: https://doi.org/10.3390/e23070820.
55. K. Acharya and H. Song, “A Comprehensive Review of Neuro-symbolic AI for Robustness, Uncertainty Quantification, and
Intervenability,” Arabian Journal for Science and Engineering, Dec. 2025, doi: https://doi.org/10.1007/s13369-025-10887-3.
56. Suriya U-ruekolan, Manot Rattananen, Jukkrapong Ponharn, and Naiyana Sahavechaphan, “Enforcing data access control and
privacy: The graph-driven data regulatory approach,” Journal of Information Security and Applications, vol. 93, pp. 104163–104163,
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57. R. Mac Ginty, “Conflict Disruption: Reassessing the Peaceandconflict System,” Journal of Intervention and Statebuilding, pp. 1–19,
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58. Betul Yurdem, Murat Kuzlu, Mehmet Kemal Gullu, Ferhat Ozgur Catak, and M. Tabassum, “Federated Learning: Overview,
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59. I. Kishor and U. Mamodiya, “Neuro-Symbolic Federated Learning Models for Diagnostic Intelligence in Healthcare 5.0,” Studies in
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60. M. Rahmati, “Federated learning for privacy-preserving AI in human–robot collaboration for smart manufacturing,” Journal of
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61. J. Liu, Y. Hu, X. Guo, T. Liang, and W. Jin, “Differential privacy performance evaluation under the condition of non-uniform noise
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63. A. Muñoz, R. Ríos, R. Román, and J. López, “A survey on the (in)security of trusted execution environments,” Computers & Security,
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64. J. A. Chandler, “Inferring Mental States from Brain Data: Ethico‐legal Questions about Social Uses of Brain Data,” Hastings Center
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65. S. Aydin, M. Melek, and L. Gökrem, “A Safe and Efficient Brain–Computer Interface Using Moving Object Trajectories and LED-
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66. G. Iovane, I. Fominska, and R. Di Pasquale, “A Neuro-Symbolic Multi-Agent Architecture for Digital Transformation of
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67. S. Vrhovec and B. Markelj, “The relation between project team conflict and user resistance in software projects,” PLOS ONE, vol.
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