Adversarial Neural Network Training for Secure and Robust Brain-to-Brain Communication

Abstract

In the rapidly evolving domain of brain-to-brain communication, safeguarding the transmission of information against adversarial threats is paramount. This study introduces an advanced approach to enhance the resilience and security of brain-to-brain communication systems utilizing electroencephalogram data against such threats through adversarial neural network training. Concentrating on event-related potentials and employing a diverse collection of eight datasets, our research rigorously evaluates and optimizes the system’s defense mechanisms against adversarial manipulations. We specifically target the optimization of trial durations and sampling rates to bolster system security. Our findings reveal a marked improvement in the system’s defensive capabilities, demonstrated by a significant increase in adversarial accuracy by 17% and enhancement in the area under the receiver operating characteristic curve by 0.12 points. These results underscore the efficacy of our approach in fortifying brain-to-brain communication systems against sophisticated cyber threats, marking a significant step forward in the secure and robust transmission of neural signals.

Keywords

• Adversarial accuracy
• adversarial attacks
• adversarial neural network training
• brainto- brain communication
• electroencephalogram
• event-related potentials