Training with brain-machine interfaces, visuo-tactile feedback and assisted locomotion improves sensorimotor, visceral, and psychological signs in chronic paraplegic patients

Abstract

Spinal cord injury (SCI) induces severe deficiencies in sensory-motor and autonomic functions and has a significant negative impact on patients’ quality of life. There is currently no systematic rehabilitation technique assuring recovery of the neurological impairments caused by a complete SCI. Here, we report significant clinical improvement in a group of seven chronic SCI patients (six AIS A, one AIS B) following a 28-month, multi-step protocol that combined training with non-invasive brain-machine interfaces, visuo-tactile feedback and assisted locomotion. All patients recovered significant levels of nociceptive sensation below their original SCI (up to 16 dermatomes, average 11 dermatomes), voluntary motor functions (lower-limbs muscle contractions plus multi-joint movements) and partial sensory function for several modalities (proprioception, tactile, pressure, vibration). Patients also recovered partial intestinal, urinary and sexual functions. By the end of the protocol, all patients had their AIS classification upgraded (six from AIS A to C, one from B to C). These improvements translated into significant changes in the patients’ quality of life as measured by standardized psychological instruments. Reexamination of one patient that discontinued the protocol after 12 months of training showed that the 16-month break resulted in neurological stagnation and no reclassification. We suggest that our neurorehabilitation protocol, based uniquely on non-invasive technology (therefore necessitating no surgical operation), can become a promising therapy for patients diagnosed with severe paraplegia (AIS A, B), even at the chronic phase of their lesion.

Non-invasive, Brain-controlled Functional Electrical Stimulation for Locomotion Rehabilitation in Individuals with Paraplegia

“…These efforts aimed at inducing activity-dependent plasticity 5 by the utilization of robotic trainers 6–8 , epidural electrical stimulation 9–11 , body-weight support trainers 12 , brain-machine interfaces 11,13–15 , transcranial magnetic stimulation 16,17 and surface functional electrical stimulation (sFES) 18,19 . Recently, our group has shown that a training protocol (the Walk Again NeuroRehabilitation protocol (WANR)) 14,20 – induced a significant level of neurological recovery in a group of eight subjects with chronic complete paraplegia (seven AIS A, one AIS B), by combining assisted walk training with robotic walkers, electroencephalography (EEG)-based brain-machine interface (BMI), and continuous visuotactile feedback. Extending our previous findings, the present study describes our group’s effort to further enhance clinical neurological recovery in severe cases of SCI by providing more selective lower-limb musculoskeletal recruitment through sFES, while maintaining our fundamental philosophy of activating both ascending and descending neural pathways during the neurorehabilitation process.…”

“…Extending our previous findings, the present study describes our group’s effort to further enhance clinical neurological recovery in severe cases of SCI by providing more selective lower-limb musculoskeletal recruitment through sFES, while maintaining our fundamental philosophy of activating both ascending and descending neural pathways during the neurorehabilitation process. As such, the BFNR (BMI, sFES, NeuroRehabilitation) protocol described here integrates four key elements to potentiate recovery in patients with SCI: muscle activation through sFES 21–23 (see 24 for a review), balance control through body weight support 25 , real-time decoding of motor command (BMI) 14,20 and sensory feedback through a portable haptic device 26 .…”

“…First, we demonstrated the validity of each aspect of our approach: a custom 16 channel sFES system, a closed-loop proportional- integral (PI) controller to cope with muscle fatigue, the integration of a portable haptic device to cope with the lack of lower-limb sensory functions and a novel BMI protocol based on the detection of left and right leg motor imagery. Next, we tested our neurorehabilitation protocol with two patients with SCI (originally AIS A) who had been previously trained with the Walk Again Neurorehabilitation (WANR) protocol and, as a consequence, converted to AIS C 20 (Supplementary Tables S1, S2). At the onset of the current protocol both patients presented motor functions more than three segments below the motor level (presence of knee extension (L3) for both patients); however, their overall motor score at that point in time was low (lower extremity motor score of 4 for P1 and 2 for P2 out of a maximum possible score of 50).…”

Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

“…Another research group explored the use of EEG signals for the decoding of gait kinematics (Presacco et al, 2011;Nakagome et al, 2020), but not gait events. In our previous work (Tortora et al, 2020), we compared the results obtained with the EEG-driven LSTM network used in this study with respect to the methods proposed by Jorquera et al (2013) and Shokur et al (2018), that involved a similar classification problem, showing significantly better performance of our approach. On the other hand, a comparison of the performance of our hybrid approach with respect to other hybrid systems in literature is difficult since, as illustrated in the section 1.1, hybrid interfaces on lower limb applications are limited to classification scenarios that are very different from the one presented in this study.…”

EEG Biomarkers Related With the Functional State of Stroke Patients

“…This approach has been employed in numerous Brain Computer Interface (BCI) systems providing real-time communication and control. BCIs have been used to control devices such as a wheelchair (Carlson and del, 2013), prosthesis or functional electrical stimulator (FES) (Ramos-Murguialday et al, 2013), sometimes in combination with immersive feedback relating to rehabilitation (Shokur et al, 2018). Over the past several years, many publications have combined BCI, FES and other feedback devices to increase cortical plasticity in stroke survivors helping them regain movement control (Dobkin, 2007).…”