DOI: https://doi.org/10.36347/sjet.2024.v12i12.011

Pages: 418-424

This study examines how Artificial Intelligence (AI) can be used to monitor and protect the transmission of neurodata by Brain-Computer Interfaces (BCIs) against hijacking and data spilling. Through updated literature as well as examining two case studies based in the Cognetivity and Emteq Labs, the paper notes the incorporation of Deep learning, homomorphic encryption, anomaly detection, and federated learning. The study uses a secondary qualitative and quantitative explanatory research design. The most important findings disclose the opportunities and issues of AI when it comes to the protection of neural data. The research suggests lightweight privacy-preservation AI models and collaboration strengthening to achieve safe, ethical consumption of BCIs in real-life settings.