The article argues that conventional performance metrics may overlook losses in intent fidelity, authorship, agency, therapeutic challenge, and durable clinical benefit. Nagarajan proposes the Slow-Fast BCI framework, which paces AI assistance according to decoder evidence, uncertainty, contextual and clinical stakes, fatigue, and user- or clinician-defined goals. It distinguishes fast assistance when intent is clear and stakes are low, guarded assistance under uncertainty, and slow assistance when misalignment could compromise safety, agency, authorship, motor learning, or therapeutic value.
The framework spans communication, motor-control, neurorehabilitation, and closed-loop neuromodulation applications, outlining corresponding safeguards and evaluation measures. The article contends that AI-mediated BCIs should be evaluated not only by decoding accuracy and task performance but also by how AI assistance is deployed: when systems act autonomously, seek confirmation, preserve user effort, or return control to the user.