Gait and balance impairments are frequently considered as the most significant concerns among individuals suffering from neurological diseases. Robot-assisted gait training (RAGT) has shown to be a promising neurorehabilitation intervention to improve gait recovery in patients following stroke or brain injury by potentially initiating neuroplastic changes. However, the neurophysiological processes underlying gait recovery through RAGT remain poorly understood. As non-invasive, portable neuroimaging techniques, electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provide new insights regarding the neurophysiological processes occurring during RAGT by measuring different perspectives of brain activity. Due to spatial information about changes in cortical activation patterns and the rapid temporal resolution of bioelectrical changes, more features correlated with brain activation and connectivity can be identified when using fused EEG-fNIRS, thus leading to a detailed understanding of neurophysiological mechanisms underlying motor behavior and impairments due to neurological diseases. Therefore, multi-modal integrations of EEG-fNIRS appear promising for the characterization of neurovascular coupling in brain network dynamics induced by RAGT. In this brief review, we surveyed neuroimaging studies focusing specifically on robotic gait rehabilitation. While previous studies have examined either EEG or fNIRS with respect to RAGT, a multi-modal integration of both approaches is lacking. Based on comparable studies using fused EEG-fNIRS integrations either for guiding non-invasive brain stimulation or as part of brain-machine interface paradigms, the potential of this methodologically combined approach in RAGT is discussed. Future research directions and perspectives for targeted, individualized gait recovery that optimize the outcome and efficiency of RAGT in neurorehabilitation were further derived.

A method of estimating the degree of active participation during stepping in a driven gait orthosis based on actuator force profile matching Banz, R; Bolliger, M; Muller, S; Santelli, C; Riener, R Banz, R;Bolliger, M;Muller, S; Santelli, C; Riener, R (2009) A method of estimating the degree of active participation during stepping in a driven gait orthosis based on actuator force profile matching AbstractVisual biofeedback with information about the patients' degree of activity is a valuable adjunct to robot-assisted gait training as means of increasing the motivation and participation of the patients during highly repetitive training sessions. In the driven gait orthosis (DGO) Lokomat, an estimation of the patient's activity level was based on man-machine interaction forces as measured at the hip and knee actuators of the exoskeletal device. In an early approach, theoretical assumptions about the expected man-machine interaction forces, due to the varying behavior of the patients, were formulated for the calculation of quantitative biofeedback. In contrast to this theory-based approach, we have developed a novel method where the biofeedback calculations were based on measured reference man-machine interaction force profiles of healthy subjects when walking with different degrees of activity. To account for intrasubject and intersubject variability, reference force profiles were processed in a model to generate multiple force profiles describing each activity state. To estimate the activity of a subject walking in the DGO, the man-machine interaction force profile was measured, matched to each of the generated force profiles, and the best fitting profile of the different activity states was identified by the smallest Euclidian distance, respectively. By calculating the difference between these Euclidian distances, a quantitative estimate of the patient's degree of activity was obtained. The novel method was evaluated and compared to the conventional approach in a study with 18 healthy subjects. This comparison showed that the novel method was more reliable in detecting different activity states and is, therefore, a promising approach for future biofeedback systems. Abstract-Visual biofeedback with information about the patients' degree of activity is a valuable adjunct to robot-assisted gait training as means of increasing the motivation and participation of the patients during highly repetitive training sessions. In the driven gait orthosis (DGO) Lokomat, an estimation of the patient's activity level was based on man-machine interaction forces as measured at the hip and knee actuators of the exoskeletal device. In an early approach, theoretical assumptions about the expected man-machine interaction forces, due to the varying behavior of the patients, were formulated for the calculation of quantitative biofeedback. In contrast to this theory-based approach, we have developed a novel method where the biofeedback calculations were based on measured reference man-machine interaction force profiles of healthy subject...