REFERENCES
1. Lassak, L.; Markert, P.; Golla, M.; Stobert, E.; Dürmuth, M. A comparative long-term study of fallback authentication schemes. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery: 2024; pp. 1-19.
2. Constantinides, A.; Belk, M.; Fidas, C.; et al. Security and usability of a personalized user authentication paradigm: insights from a longitudinal study with three healthcare organizations. ACM. Trans. Comput. Healthc. 2023, 4, 1-40.
3. He, H.; Liao, R.; Li, Y. MSAFNet: a novel approach to facial expression recognition in embodied AI systems. Intell. Robot. 2025, 5, 313-32.
4. Nakamura, T.; Goverdovsky, V.; Mandic, D. P. In-ear EEG biometrics for feasible and readily collectable real-world person authentication. IEEE. Trans. Inf. Forensics. Secur. 2018, 13, 648-61.
5. Aldayel, M.; Alsedairy, N.; Al-Nafjan, A.; Alsenan, S. Systematic review of brain-computer interface based user authentication system: trends, challenges, and directions. IEEE. Access. 2024, 12, 96848-61.
6. Seyfizadeh, A.; Peach, R. L.; Tovote, P.; Isaias, I. U.; Volkmann, J.; Muthuraman, M. Enhancing security in brain–computer interface applications with deep learning: electroencephalogram-based user identification. Expert. Syst. Appl. 2024, 253, 124218.
7. Colafiglio, T.; Lofù, D.; Sorino, P.; et al. EmoSynth real time emotion-driven sound texture synthesis via brain-computer interface. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP). Association for Computing Machinery: 2024; pp. 616-21.
8. Białas, K.; Kedziora, M.; Chałupnik, R.; Song, H. H. Multifactor authentication system using simplified EEG brain–computer interface. IEEE. Trans. Hum. Mach. Syst. 2022, 52, 867-76.
9. Peng, Y.; Jiang, Y.; Zuo, Z.; Gu, S.; Wang, Z.; Tian, Z. A rehabilitation design concept based on brain–computer interface and McKibben artificial muscle. Healthc. Rehabil. 2026, 2, 100066.
10. Bidgoly, A. J.; Bidgoly, H. J.; Arezoumand, Z. A survey on methods and challenges in EEG based authentication. Comput. Secur. 2020, 93, 101788.
11. Jin, J.; Chen, Z.; Cai, H.; Pan, J. Affective EEG-based person identification with continual learning. IEEE. Trans. Instrum. Meas. 2024, 73, 1-16.
12. Chaurasia, A. K.; Fallahi, M.; Strufe, T.; Terhörst, P.; Cabarcos, P. A. NeuroIDBench: an open-source benchmark framework for the standardization of methodology in brainwave-based authentication research. J. Inf. Secur. Appl. 2024, 85, 103832.
13. Nguyen, Q. T.; Le, L.; Williams-King, D.; Tang, Q. H.; Duong-Trung, N. Explainable AI for dementia detection using EEG microstates. In Companion Proceedings of the 31st International Conference on Intelligent User Interfaces. Association for Computing Machinery: 2026; pp. 39-42.
14. Adebisi, A. T.; Lee, H. W.; Veluvolu, K. C. EEG-based brain functional network analysis for differential identification of dementia-related disorders and their onset. IEEE. Trans. Neural. Syst. Rehabil. Eng. 2024, 32, 1198-209.
15. Hogges, J.; Shahriar, H.; Sneha, S.; Ahamed, S. A two-step password authentication system for Alzheimer patients. In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, July 13-17, 2020. IEEE: 2020; pp. 1444-8.
16. Raivio, M. M.; Mäki-Petäjä-Leinonen, A. P.; Laakkonen, M. L.; Tilvis, R. S.; Pitkälä, K. H. The use of legal guardians and financial powers of attorney among home-dwellers with Alzheimer’s disease living with their spousal caregivers. J. Med. Ethics. 2008, 34, 882-6.
17. Gaikwad, S.; Senapati, S.; Haque, M. A.; Kayed, R. Senescence, brain inflammation, and oligomeric tau drive cognitive decline in Alzheimer’s disease: evidence from clinical and preclinical studies. Alzheimers. Dement. 2024, 20, 709-27.
18. Hughes, J.; Pastrana, S.; Hutchings, A.; et al. The art of cybercrime community research. ACM. Comput. Surv. 2024, 56, 1-26.
19. Lassi, M.; Fabbiani, C.; Mazzeo, S.; et al. Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: early biomarkers along the Alzheimer’s disease continuum? NeuroImage. Clin. 2023, 38, 103407.
20. Huber, M.; Luu, A. T.; Boutros, F.; Kuijper, A.; Damer, N. Bias and diversity in synthetic-based face recognition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 2024; pp. 6215-26.
21. Drozdowski, P.; Rathgeb, C.; Dantcheva, A.; Damer, N.; Busch, C. Demographic bias in biometrics: a survey on an emerging challenge. IEEE. Trans. Technol. Soc. 2020, 1, 89-103.
22. Pappula, M.; Anwar, S. M. An ADHD diagnostic interface based on EEG spectrograms and deep learning techniques. In 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM), Antigua, Guatemala, Nov 13-15, 2024. IEEE: 2024; p. 1-4.
23. Haider, C. M. R.; Clifton, C.; Zhou, Y. Unfair AI: it isn’t just biased data. In 2022 IEEE International Conference on Data Mining (ICDM), Orlando, USA, Nov 28 - Dec 01, 2022. IEEE: 2022; pp. 957-62.
24. Sun, J.; Sun, Y.; Shen, A.; Li, Y.; Gao, X.; Lu, B. An ensemble learning model for continuous cognition assessment based on resting-state EEG. NPJ. Aging. 2024, 10, 1.
25. Sun, C.; Song, M.; Cai, D.; Zhang, B.; Hong, S.; Li, H. A systematic review of echo state networks from design to application. IEEE. Trans. Artif. Intell. 2024, 5, 23-37.
26. Wang, Z.; Sun, J. TransTab: learning transferable tabular transformers across tables. Adv. Neural. Inf. Process. Syst. 2022, 35, 2902-15.
27. Shwartz-Ziv, R.; Armon, A. Tabular data: deep learning is not all you need. Inf. Fusion. 2022, 81, 84-90.
28. Goetz, L.; Seedat, N.; Vandersluis, R.; van der Schaar, M. Generalization - a key challenge for responsible AI in patient-facing clinical applications. NPJ. Digit. Med. 2024, 7, 126.
29. Chen, Y.; Atnafu, A. D.; Schlattner, I.; et al. A high-security EEG-based login system with RSVP stimuli and dry electrodes. IEEE. Trans. Inf. Forensics. Secur. 2016, 11, 2635-47.
30. Wu, Q.; Yan, B.; Zeng, Y.; Zhang, C.; Tong, L. Anti-deception: reliable EEG-based biometrics with real-time capability from the neural response of face rapid serial visual presentation. BioMed. Eng. OnLine. 2018, 17, 55.
31. Mu, Z.; Hu, J.; Min, J. EEG-based person authentication using a fuzzy entropy-related approach with two electrodes. Entropy 2016, 18, 432.
32. Wu, Q.; Zeng, Y.; Zhang, C.; Tong, L.; Yan, B. An EEG-based person authentication system with open-set capability combining eye blinking signals. Sensors 2018, 18, 335.
33. Thomas, K. P.; Vinod, A. P. EEG-based biometric authentication using gamma band power during rest state. Circuits. Syst. Signal. Process. 2018, 37, 277-89.
34. Kumar, P.; Saini, R.; Kaur, B.; Roy, P. P.; Scheme, E. Fusion of neuro-signals and dynamic signatures for person authentication. Sensors 2019, 19, 4641.
35. Zeynali, M.; Seyedarabi, H. EEG-based single-channel authentication systems with optimum electrode placement for different mental activities. Biomed. J. 2019, 42, 261-7.
36. Seha, S. N. A.; Hatzinakos, D. EEG-based human recognition using steady-state AEPs and subject-unique spatial filters. IEEE. Trans. Inf. Forensics. Secur. 2020, 15, 3901-10.
37. Rathi, N.; Singla, R.; Tiwari, S. A novel approach for designing authentication system using a picture based P300 speller. Cogn. Neurodyn. 2021, 15, 805-24.
38. Yap, H. Y.; Choo, Y. H.; Mohd Yusoh, Z. I.; Khoh, W. H. An evaluation of transfer learning models in EEG-based authentication. Brain. Inform. 2023, 10, 19.
39. Alsumari, W.; Hussain, M.; Alshehri, L.; Aboalsamh, H. A. EEG-based person identification and authentication using deep convolutional neural network. Axioms 2023, 12, 74.
40. Ferdi, A. Y.; Ghazli, A. Authentication with a one-dimensional CNN model using EEG-based brain-computer interface. Comput. Methods. Biomech. Biomed. Eng. 2025, 28, 1969-80.
41. Bolouri, S.; Shukla, D. An EEG-based user authentication system using event-related potentials and ensemble learning. In 2024 Cyber Awareness and Research Symposium (CARS), Grand Forks, USA, Oct 28-29, 2024. IEEE: 2024; p. 1-6.
42. Gong, Y.; Wang, M.; Zhang, Y.; Zhang, W.; Pang, S. A unified deep learning-based EEG biometric authentication system for cross-session scenarios. In International Conference on Advanced Data Mining and Applications. Springer: 2024; pp. 48-62.
43. Mishra, A. R.; Kumar, R.; Saini, R. Performance enhancement of EEG signatures for person authentication using CNN BiLSTM method. J. Univ. Comput. Sci. 2024, 30, 1755-79.
44. Valdes-Sosa, P. A.; Galan-Garcia, L.; Bosch-Bayard, J.; et al. The Cuban Human Brain Mapping Project, a young and middle age population-based EEG, MRI, and cognition dataset. Sci. Data. 2021, 8, 45.
45. Miltiadous, A.; Tzimourta, K. D.; Afrantou, T.; et al. A dataset of scalp EEG recordings of Alzheimer’s disease, frontotemporal dementia and healthy subjects from routine EEG. Data 2023, 8, 95.
46. Prado, P.; Medel, V.; Gonzalez-Gomez, R.; et al. The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds. Sci. Data. 2023, 10, 889.
47. Dzianok, P.; Kublik, E. PEARL-Neuro Database: EEG, fMRI, health and lifestyle data of middle-aged people at risk of dementia. Sci. Data. 2024, 11, 276.
48. Chu, C.; Zhang, Z.; Song, Z.; et al. An enhanced EEG microstate recognition framework based on deep neural networks: an application to Parkinson’s disease. IEEE. J. Biomed. Health. Inform. 2023, 27, 1307-18.
49. Antonova, E.; Holding, M.; Suen, H. C.; Sumich, A.; Maex, R.; Nehaniv, C. EEG microstates: functional significance and short-term test-retest reliability. Neuroimage. Rep. 2022, 2, 100089.
50. Nguyen, Q. T. Standardising number of EEG sensors for AI-driven dementia detection. IEEE. Sens. Lett. 2025, 9, 1-4.
51. Nguyen, Q. T.; Le, L.; Tran, X. T. et. al. Transforming brainwaves into language: EEG microstates meet text embedding models for dementia detection. In The 63rd Annual Meeting of the Association for Computational Linguistics, Vienna, Austria. Association for Computational Linguistics: 2025; pp. 186-202.
52. Tarailis, P.; Koenig, T.; Michel, C. M.; Griškova-Bulanova, I. The functional aspects of resting EEG microstates: a systematic review. Brain. Topogr. 2024, 37, 181-217.
53. Khanna, A.; Pascual-Leone, A.; Farzan, F. Reliability of resting-state microstate features in electroencephalography. PloS. One. 2014, 9, e114163.
54. Koenig, T.; Kottlow, M.; Stein, M.; Melie-García, L. Ragu: a free tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics. Comput. Intell. Neurosci. 2011, 2011, 938925.
55. Pascual-Marqui, R. D.; Michel, C. M.; Lehmann, D. Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE. Trans. Biomed. Eng. 1995, 42, 658-65.
56. Bagdasarov, A.; Roberts, K.; Bréchet, L.; Brunet, D.; Michel, C. M.; Gaffrey, M. S. Spatiotemporal dynamics of EEG microstates in four-to eight-year-old children: age-and sex-related effects. Dev. Cogn. Neurosci. 2022, 57, 101134.
57. Valt, C.; Tavella, A.; Berchio, C.; et al. MEG microstates: an investigation of underlying brain sources and potential neurophysiological processes. Brain. Topogr. 2024, 37, 993-1009.
58. Zhao, X.; Li, Z.; Zhao, C.; Fu, R.; Wang, C. Distraction-level recognition based on stacking ensemble learning for IVIS secondary tasks. Expert. Syst. Appl. 2024, 244, 122849.
59. Vaswani, A.; Shazeer, N.; Parmar, N.; et al. Attention is all you need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, USA, Dec 4-9, 2017. 2017; pp. 5998-6008. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html. (accessed 2026-06-25).
61. Yan, M.; Huang, C.; Bienstman, P.; Tino, P.; Lin, W.; Sun, J. Emerging opportunities and challenges for the future of reservoir computing. Nat. Commun. 2024, 15, 2056.
62. Wootton, A. J.; Taylor, S. L.; Day, C. R.; Haycock, P. W. Optimizing echo state networks for static pattern recognition. Cogn. Comput. 2017, 9, 391-9.
63. Amin, E.; Elgammal, Y. M.; Zahran, M. A.; Abdelsalam, M. M. Alzheimer’s disease: new insight in assessing of amyloid plaques morphologies using multifractal geometry based on Naive Bayes optimized by random forest algorithm. Sci. Rep. 2023, 13, 18568.
64. Yuan, Y.; Wang, W.; Guo, Q.; Xiong, Y.; Shen, C.; He, P. Does ChatGPT know that it does not know? Evaluating the black-box calibration of ChatGPT. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). ELRA and ICCL: 2024; pp. 5191-201. https://aclanthology.org/2024.lrec-main.462/. (accessed 2026-06-25).
66. Pessach, D.; Shmueli, E. A review on fairness in machine learning. ACM. Comput. Surv. 2022, 55, 1-44.
67. Férat, V.; Scheltienne, M.; Brunet, D.; Ros, T.; Michel, C. Pycrostates: a Python library to study EEG microstates. J. Open. Source. Softw. 2022, 7, 4564.
68. Fisher, R. A. Statistical methods for research workers. 1934. https://archive.org/details/in.ernet.dli.2015.205971. (accessed 2026-06-25).
69. Stouffer, S. A.; Suchman, E. A.; DeVinney, L. C.; Star, S. A.; Williams, R. M. The American soldier: adjustment during army life. 1949. https://gwern.net/doc/psychology/1949-stouffer-theamericansoldier-v1-adjustmentduringarmylife.pdf. (accessed 2026-06-25).
70. Goeman, J. J.; Solari, A. Multiple hypothesis testing in genomics. Stat. Med. 2014, 33, 1946-78.
73. Nguyen, Q. T.; Huiru, Z.; Tazin, T.; et al. Emotion recognition using text embedding models: wearable and wireless EEG without fixed EEG channel configurations. In Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery: 2025; pp. 476-88.
74. Zeng, Y.; Wu, Q.; Yang, K.; et al. EEG-based identity authentication framework using face rapid serial visual presentation with optimized channels. Sensors 2018, 19, 6.
75. Ortega-Rodríguez, J.; Gómez-González, J. F.; Pereda, E. Selection of the minimum number of EEG sensors to guarantee biometric identification of individuals. Sensors 2023, 23, 4239.
76. Gorur, K.; Olmez, E.; Ozer, Z.; Cetin, O. EEG-driven biometric authentication for investigation of fourier synchrosqueezed transform-ICA robust framework. Arab. J. Sci. Eng. 2023, 48, 10901-23.
