Neuro-Assisted Therapy - A clinical-epistemic architecture for dynamic psychophysiological mapping, explainable artificial intelligence, and multimodal therapeutic inquiry

Abstract

This article proposes Neuro-Assisted Therapy (NAT), an experimental clinical architecture integrating therapeutic conversation, naturalistic or controlled-provocative experiences, multimodal psychophysiological acquisition, and explainable artificial intelligence. Its aim is not to read thoughts, reveal unconscious contents, or automate diagnosis. The proposal is to identify intraindividual response configurations - contextual patterns of neural, autonomic, behavioral, linguistic, and experiential change - and turn them into traceable, revisable clinical hypotheses subject to joint interpretation by therapist and patient. The model separates measurement, inference, and interpretation; alternates naturalistic sessions, controlled exploration, and dialogical return; and prioritizes personal baselines and longitudinal analysis. A preliminary reconnaissance shows that clinical EEG, event-related potentials, neurofeedback, virtual-reality psychotherapy, multimodal affective computing, and AI assistants havesubstantial antecedents, including patented combinations. The intended contribution lies not in adding these components together, but in organizing them under a protocol of responsive mapping, evidential provenance, and shared clinical governance. Principles, functional architecture, limitations, risks, and a staged experimental program are presented.