HOME & AMBULATORY CLOSED-LOOP NEURAL RECOVERY AT THE LIMIT HOME & AMBULATORY CLOSED-LOOP NEURAL RECOVERY AT THE LIMIT is a large-scale research monograph on the transition of adaptive neural recovery systems from controlled laboratories into homes, ambulatory settings, and everyday life. It asks a deceptively difficult question: what happens to the scientific meaning of a closed loop when the environment stops cooperating with the experiment?

Abstract

HOME & AMBULATORY CLOSED-LOOP NEURAL RECOVERY AT THE LIMIT HOME & AMBULATORY CLOSED-LOOP NEURAL RECOVERY AT THE LIMIT is a large-scale research monograph on the transition of adaptive neural recovery systems from controlled laboratories into homes, ambulatory settings, and everyday life. It asks a deceptively difficult question: what happens to the scientific meaning of a closed loop when the environment stops cooperating with the experiment? The book is organized around the Three-Coupling System Ecological Robustness x Low-Burden Adaptation x Valued Function. Its book-defined heuristic, HACR, links ecological robustness, low-burden adaptation, valued function, and exitability while exposing burden, context shift, and equity or access gaps as explicit system costs. The heuristic is a research-design instrument, not a biological law, clinical score, treatment rule, or regulatory standard. Across 82 chapters, the volume studies naturalistic neural signals, context-aware intent and recovery-state inference, self-calibration, fatigue and readiness, safe abstention, pause and rollback logic, caregiver and therapist effects, connectivity loss, hardware wear, device migration, offline operation, remote quality control, valued activities, participation, unassisted transfer, equity, privacy, selective logging, portability, exitability, multicenter replication, service continuity, and successor-ready research records. Home deployment is treated not as laboratory deployment at a new address, but as a new causal regime in which routines, access, maintenance, support networks, competing obligations, and environmental variability can change both the intervention and its interpretation. The mathematical architecture intentionally uses diverse forms including state-space models, coupled dynamics, Lyapunov and control-barrier functions, Bayesian inference, information theory, optimal control, model-predictive control, causal contrasts, survival and hazard models, random effects, graph Laplacians, spectral stability, reliability models, reachability sets, Pareto frontiers, and other conceptual engineering structures. These equations are used to expose assumptions and design falsifiable experiments; they are not presented as validated laws of neural physiology. The volume concludes with 200 Research Gates and 33 Answer Embryos. The Gates turn unresolved deployment problems into successor-ready research programs. The Answer Embryos offer provisional, falsifiable syntheses designed to be decomposed, revised, combined, or reborn as evidence changes. The central discipline remains constant throughout: prediction is not mechanism, task completion is not independent recovery, home adherence is not proof of benefit, and a useful closed loop should preserve human override, graceful degradation, transparent failure, practical exitability, and the ability of future researchers to reconstruct why the system behaved as it did. This book is intended for researchers in brain-computer interfaces, neurorehabilitation, adaptive control, assistive technology, neuroengineering, digital health, human-machine interaction, rehabilitation robotics, FES research, causal inference, and long-horizon health infrastructure who are interested in making closed-loop neural systems scientifically interpretable beyond the laboratory.