Neuro shield : Securing neural data with privacy-preserving algorithms for brain computer interface
Kaku Riya Dharmendrariya.kaku119353@marwadiuniversity.ac.inDepartment of Computer EngineeringMarwadi UniversityRajkot, Gujarat, 360003, India0009-0006-2662-5019View full profile → , *Deepak Kumar VermaCorresponding authordeepak.verma1980@gmail.comDepartment of Computer EngineeringMarwadi UniversityRajkot, Gujarat, 360003, India0000-0002-4745-1752View full profile → , Satvik Vatssvcse@mmmut.ac.inDepartment of Computer Science & EngineeringMadan Mohan Malaviya University of TechnologyGorakhpur, Uttar Pradesh, 273010, India0000-0002-9422-4915View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Aug 2025
- Published Online:
- 29 May 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2323
- Pages:
- 2263–2279
Abstract
Keywords
Subject Classifications
References
[1] S. Saha, K. A. Mamun, K. Ahmed, R. Mostafa, G. R. Naik, S. Darvishi, and Y. Fukuda, “Intra-and inter-subject variability in EEG-based brain–computer interface: A review,” Frontiers in Computational Neuroscience, vol. 13, Art. no. 87 (2019), doi: 10.3389/fncom. 2019.00087.
[2] T. Jia, L. Meng, S. Li, J. Liu, and D. Wu, “Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 3442-3451 (2024), doi: 10.1109/TNSRE.2024.3457504.
[3] R. Liu, Y. Chen, A. Li, Y. Ding, H. Yu, and C. Guan, “Aggregating intrinsic information to enhance BCI performance through federated learning,” Neural Networks, vol. 172, Art. no. 106100 (2024), doi: 10.1016/j.neunet.2024.106100.
[4] J. Wu, Y. Chen, and M. Zhang, “Hierarchical FL for EEG-based BCI: A Personalization Framework,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 450–460 (2023).
[5] Y. Jia, L. Zhou, and F. Lin, “Federated Transformers for EEG Motor Imagery Classification in BCI Applications,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1234–1238 (2024).
[6] H. Kim, D. Lee, and S. Park, “Differential Privacy-Aware FL for EEG-based Brain–Computer Interfaces,” IEEE Internet of Things Journal, vol. 10, no. 1, pp. 155–165 (2023).
[7] X. Lin, Y. Qian, and L. Zhao, “Contrastive FL for EEG Classification with Limited Labels,” in Proc. IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM), pp. 998–1003 (2024).
[8] R. Zhou, A. Sun, and J. Li, “Subject-Specific FL for BCI with EEG Signals,” IEEE Sensors Journal, vol. 23, no. 5, pp. 6789–6796 (2023).
[9] A. Rao, V. Srinivasan, and P. Roy, “Graph Neural Network-Based FL for EEG Signal Classification,” IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 1, pp. 215–224 (2024).
[10] M. Hasan, F. Ahmed, and N. Rahman, “Mobile-Friendly FL with Lightweight CNN for Real-Time BCI Applications,” IEEE Access, vol. 13, pp. 45678–45688 (2025).
[11] S. Patel, R. Kumar, and A. Sinha, “Hybrid Transfer Learning and FL for EEG Classification,” in Proc. IEEE Int. Conf. on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp. 145–150 (2024).
[12] Y. M. Huang, H. N. Hung, and V. S. Tseng, “Privacy-preserving attention-weighted multi-source domain adaptation for EEG motor imagery,” in Proc. 2024 IEEE Int. Conf. Acoustics, Speech, Signal Process. (ICASSP), pp. 7470–7474 (Apr. 2024), doi: 10.1109/ICASSP49456.2024.10098321.
[13] R. Singh, A. Mehta, and P. Verma, “Cluster-Based FL for Heterogeneous EEG Motor Imagery Data,” IEEE Transactions on Artificial Intelligence, vol. 3, no. 2, pp. 112–120 (2025).
[14] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Proc. 20th Int. Conf. Artificial Intelligence and Statistics (AISTATS), pp. 1273–1282 (Apr. 2017).
[15] G. Schalk, “EEG Motor Movement/Imagery Dataset,” PhysioNet (Sep. 2009). [Online]. Available: https://physionet.org/content/eegmmidb/1.0.0/. [Accessed: Feb. 21, 2025].
[16] V. J. Lawhern, A. J. Solon, N. R. Waytowich, S. M. Gordon, C. P. Hung, and B. J. Lance, “EEGNet: A compact convolutional neural network for EEG-based brain–computer interfaces,” Journal of Neural Engineering, vol. 15, no. 5, p. 056013 (2018), doi: 10.1088/1741-2552/aace8c.
[17] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” Proceedings of Machine Learning and Systems, vol. 2, pp. 429-450 (2020).
[18] R. T. Schirrmeister, J. T. Springenberg, L. D. J. Fiederer, M. Glasstetter, K. Eggensperger, M. Tangermann, F. Hutter, W. Burgard, and T. Ball, “Deep Learning with Convolutional Neural Networks for EEG Decoding and Visualization,” Hum. Brain Mapp., vol. 38, no. 11, pp. 5391–5420 (2017).
[19] Z. Hu, L. Chen, Y. Luo, and J. Zhou, “EEG-based emotion recognition using a convolutional recurrent neural network with multi-head self-attention,” Applied Sciences, vol. 12, no. 21, Art. no. 11255 (2022), doi: 10.3390/app122111255.
[20] M. A. Abdulhussein, X. An, A. A. Alsakaa, and D. Ming, “Lack of habituation in migraine patients and Evoked Potential types: Analysis study from EEG signals,” Journal of Information and Optimization Sciences, vol. 43, no. 4, pp. 855–891 (2022).
[21] S. Gautam, A. Khunteta, and D. Ghosh, “Adaptive optimization framework for multimodal software defect prediction using reinforcement learning,” Journal of Information and Optimization Sciences, vol. 46, no. 7, pp. 2351–2362 (2025).
