Brain Signal Security and Ethical Considerations in ML/DL-based BCIs

Abstract

Machine Learning (ML) and Deep Learning (DL)-based Brain–Computer Interfaces (BCIs) have dramatically transformed human-technology interaction, enabling applications that range from assistive technologies to neurorehabilitation. The application of ML/DL-based BCIs brings serious concerns related to brain signal security and ethics. These are brain signals; hence, by nature, electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) can reveal personal information regarding an individual’s mental state, intentions, and health. Therefore, unauthorized access, manipulation, or misuse of such data poses serious risks, such as privacy breaches, identity theft, and malicious control over neuroprosthetics. This paper investigates a set of critical security vulnerabilities in ML/DL-based BCIs, adversarial attacks, data poisoning, and model inversion, which may simultaneously violate the integrity and confidentiality of the brain data. If this is so, then ethical considerations include informed consent, ownership of data, and fair access to BCI technologies. The paper argues for incorporating robust encryption methods, secure data transmission protocols, and adversarial training of BCIs to protect against emerging threats. It also outlines an ethical compliance framework that focuses on transparency, accountability, and adherence to human rights principles. In this context, the challenges presented above must be addressed as BCI technology is translated from the research environment into widespread application to ensure safe, fair, and trustworthy integration into society. This work seeks to stimulate debate among researchers, practitioners, and policymakers in pushing for interdisciplinary approaches to balance innovation with the demands of ethics and security in this transformative technology.