Version 1
: Received: 3 July 2024 / Approved: 3 July 2024 / Online: 4 July 2024 (14:29:03 CEST)
How to cite:
Ahmadi, H.; Kuhestani, A.; Keshavarzi, M.; Mesin, L. Enhanced Brain-to-Brain Communication Security via Adversarial Neural Network Training. Preprints2024, 2024070392. https://doi.org/10.20944/preprints202407.0392.v1
Ahmadi, H.; Kuhestani, A.; Keshavarzi, M.; Mesin, L. Enhanced Brain-to-Brain Communication Security via Adversarial Neural Network Training. Preprints 2024, 2024070392. https://doi.org/10.20944/preprints202407.0392.v1
Ahmadi, H.; Kuhestani, A.; Keshavarzi, M.; Mesin, L. Enhanced Brain-to-Brain Communication Security via Adversarial Neural Network Training. Preprints2024, 2024070392. https://doi.org/10.20944/preprints202407.0392.v1
APA Style
Ahmadi, H., Kuhestani, A., Keshavarzi, M., & Mesin, L. (2024). Enhanced Brain-to-Brain Communication Security via Adversarial Neural Network Training. Preprints. https://doi.org/10.20944/preprints202407.0392.v1
Chicago/Turabian Style
Ahmadi, H., MohammadReza Keshavarzi and Luca Mesin. 2024 "Enhanced Brain-to-Brain Communication Security via Adversarial Neural Network Training" Preprints. https://doi.org/10.20944/preprints202407.0392.v1
Abstract
Brain-to-brain communication (B2B-C) is rapidly expanding, integrating communication technology and neuroscience to enable direct neural data transfer between people. Nonetheless, because neural data is susceptible to noise, interference, and hostile attacks, guaranteeing the security and resilience of B2B-C systems continues to be difficult. This work aims to use Adversarial Neural Network Training (ANNT) to improve the security of B2B-C systems by using Steady-State Visually Evoked Potentials (SSVEP) EEG data. We use two large SSVEP datasets for a thorough analysis: Lee2019_SSVEP and Nakanishi2015. We use the Fast Gradient Sign Method (FGSM) to create adversarial instances and ANNT to train the model on clean and adversarially perturbed data. The system's accuracy and resilience are significantly improved by ANNT, as seen by the up to 17% increase in adversarial accuracy and the average 0.03 point improvement in the Area Under the Curve (AUC). This work demonstrates how ANNT may strengthen B2B-C systems against advanced cyberattacks, opening the door for dependable and safe neural communication technologies.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
