From neural signal to cognitive inference: Legal, ethical, and governance implications of machine learning in neurodata - a case study of cognitive state detection

This paper examines the legal, ethical, and governance implications of Machine Learning (ML) systems applied to neural data, with a particular focus on the emerging capacity to infer cognitive states from brain signals. While advances in neurotechnology are often framed within clinical and therapeutic contexts, the increasing use of ML techniques to analyse electroencephalogram (EEG) data raises broader concerns regarding the extraction and interpretation of highly sensitive neural information. Using a constrained Dynamic Mode Decomposition pipeline for cognitive state detection as a case study, this paper demonstrates how algorithmic analysis of neural signals enables forms of cognitive inference that extend beyond immediate medical purposes. Building on this technical foundation, the paper critically evaluates the adequacy of existing legal frameworks in addressing the unique challenges posed by neurodata. It argues that current data protection and privacy regimes, such as those governing sensitive personal data, are ill-equipped to regulate not only the collection of neural data but also the inferences derived from it. Particular attention is given to issues of informed consent, ownership of neural data, liability for erroneous or harmful inferences, and the distinction between raw data and algorithmically generated insights. The paper further explores the ethical implications of neurodata analytics, including threats to mental privacy, cognitive liberty, and individual autonomy. It highlights the risks of misuse in contexts extending beyond healthcare, such as surveillance, employment, and behavioural profiling. From a governance perspective, the analysis identifies significant regulatory gaps and fragmentation across existing legal regimes, compounded by disparities in institutional capacity in Global South and Small Island Developing State (SIDS) contexts. The paper concludes by proposing the need for a more coherent and anticipatory regulatory approach to neurotechnology and ML, grounded in interdisciplinary collaboration and responsive to both technological developments and socio-legal realities. By situating a technical case study within a broader normative framework, this research contributes to ongoing debates on the governance of emerging technologies at the intersection of law, ethics, and cognitive science.