Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery

TLDR

Brain–computer interfaces combined with robot‑assisted physical therapy are a promising neurorehabilitation strategy for severe hemiparetic patients, but it remains unclear how artificially closing the sensorimotor feedback loop affects BCI decoding performance. This study investigates whether providing haptic feedback via a robotic arm improves online decoding of arm movement intention in healthy subjects and stroke patients. The authors evaluated decoding performance in six healthy participants and two stroke patients while delivering haptic feedback through a seven‑degree‑of‑freedom robotic arm. Empirical results show that haptic feedback enhances online decoding accuracy, supporting the feasibility of combining robot‑assisted therapy with BCIs for future rehabilitation.

Abstract

The combination of brain–computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.