Active mental engagement and a positive emotional state are prerequisites for optimal outcomes of rehabilitation programs for stroke patients. Our program at the ETH, Zurich utilizes a closed loop response in automated robot-assist gait training coupled with virtual reality provided tasks. Heart rate variability has been shown to be sensitive to cognitive as well as emotional states as well as pathophysiological-environmental challenges. We investigated whether adaptation to a task differs between stroke patients with either cortical or subcortical lesions. Seven non-stroke control participants were compared to responses of nine stroke patients with either a diagnosis of cortical or subcortical stroke using heart rate variability. The robot-assist virtual reality training session consisted of a familiarization period, a baseline walking period, an under-challenged, appropriate challenged and over-challenged condition. Time and frequency domain as well as nonlinear features were assessed. Our results indicated that only entropy was sensitive to identifying adaptation to a different level of difficulty. Thus a significant difference was seen between the three stroke groups and control for adaption from baseline to the under-challenged condition (p=0.026), and also from the under-challenged condition to an appropriate level of challenge (p=0.027). We propose that the entropy feature provides a robust index of cognitive and emotional level associated with task difficulty experienced by post-stroke patients that allows real time closed loop regulation of robot-assist gait rehabilitation.