Inference from Neural Signals

Definition

Inference from Neural Signals examines limits and validity of conclusions drawn from neural data beyond the original purpose within the broader domain of the privacy, ownership, inference, retention and protection of neural and mental-state data.

Why this matters

Failures concerning limits and validity of conclusions drawn from neural data beyond the original purpose can cause physical or psychological harm, loss of function, privacy invasion, identity compromise, exclusion, coercion or irreversible impact on human agency.

Research questions

Under which conditions can limits and validity of conclusions drawn from neural data beyond the original purpose be delivered, measured or protected reliably, and how do outcomes vary by person, device, duration, context and governance controls?

Hypotheses

A transparent, safety-bounded and person-centred approach to inference from neural signals, combining validated measurement, informed consent, privacy and security controls, human oversight and longitudinal monitoring, will improve benefit–risk outcomes compared with opaque or technology-centred approaches.

Proposed methods

privacy threat modelling; neural inference testing; consent comprehension studies; data-flow mapping; security assessment; policy analysis; user-rights evaluation; expert and affected-user review; reproducibility testing; methods adapted specifically to Inference from Neural Signals

Stakeholders and beneficiaries

neurotechnology users; patients; researchers; clinicians; privacy regulators; device manufacturers; employers; insurers; civil society