A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation - Lennon, Olive; Tonellato, Michele; Del Felice, Alessandra; Di Marco, Roberto; Fingleton, Caitriona; Korik, Attila; Guanziroli, Eleonora; Molteni, Franco; Guger, Christoph; Otner, Rupert; Coyle, Damien (Frontiers in Neuroscience, 2020)
Background: Stroke is a disease with a high associated disability burden. Robotic assisted gait training offers opportunity for the practice intensity levels associated with good functional walking outcomes in this population. Neural interfacing technology, electroencephalography (EEG) or electromyography (EMG) can offer new strategies for robotic gait re-education after stroke by promoting more active engagement in movement intent and/or neurophysiological feedback. Objectives: This study identifies the current state-of-the-art and limitations in direct neural interfacing with robotic gait devices in stroke rehabilitation. Methods: A pre-registered systematic review was conducted using standardized search operators that included the presence of stroke and robotic gait training and neural biosignals (EMG and/or EEG) and was not limited by study type. Results: From a total of 8899 papers identified, 13 articles were considered for final selection. Only five of the 13 studies received a strong or moderate quality rating as a clinical study. Three studies recorded EEG activity during robotic gait; two of which used EEG for BCI purposes. While demonstrating utility for decoding kinematic and EMG related gait data, no EEG study identified closed the loop between robot and human. Twelve of the studies recorded EMG activity during or after robotic walking, primarily as an outcome measure. One study used multisource information fusion from EMG, joint angle and force to modify robotic commands in real time, with higher error rates observed during active movement. A novel study identified used EMG data during robotic gait to derive the optimal, individualised robot driven step trajectory. Conclusions: Wide heterogeneity in the reporting and purpose of neurobiosignal use during robotic gait training after stroke exists. Neural interfacing with robotic gait after stroke demonstrates promise as a future field of study. However, as a nascent area, direct neural interfacing with robotic gait after stroke would benefit from a more standardised protocol for biosignal collection and processing and for robotic deployment. Appropriate reporting for clinical studies of this nature is also required with respect to study type and the participants’ characteristics.