Spinal Cord Injury (SCI) affects almost 500,000 people every year, causing complete paralysis of both legs in severe cases, with no current treatment perspective. However, new neuroengineering technologies, such as the Brain Spine Interface (BSI), have emerged to potentially alleviate paralysis and promote neurological recovery. The BSI aims to establish a digital bridge between the motor cortex and dormant neurons in the spinal cord, enabling volitional control over muscle activity, restoring a more natural and adaptive control of standing and walking in people with paralysis. This technology could have a significant impact on the human, societal, and economic costs associated with SCI.
As the first part of this thesis, an advanced framework and machine learning algorithms were developed to perform online decoding of locomotor features from a non-invasive Electroencephalography (EEG)-based mobile decoding platform High-resolution recordings showed event-related desynchronization and synchronization patterns in beta and gamma bands time-locked to the gait cycle in source-localized sensorimotor areas, as previously described in the literature.
The possibility of developing a BSI from EEG recordings was validated. The potential of this platform for neurorehabilitation in SCI participants was investigated to trigger EES programs to restore walking. However, the platform's limitations were observed due to low signal-to-noise ratio, exogenous signals, and low performance in classifying gait events, making it insufficient for restoring mobility.
The EEG-based Brain Spine Interface was found insufficient to restore locomotion in SCI participants, but its potential for single joint movement was questioned. A simplified setup was developed and tested in four SCI participants. After 20 training sessions, the decoding performance was excellent, with up to 100% accuracy in some tasks. Participants reported positive feedback and a sensation of regaining control of their paralyzed limbs. Although clinical outcomes were not conclusive, participants were enthusiastic about the potential of the platform for rehabilitation training.
We investigated using ECoG recordings and spinal cord stimulation as a BSI for severe SCI patients. A clinical trial was conducted using the fully implantable epidural ECoG-based system. Motor intentions was decoded from ECoG recordings and translated into commands to modulate EES. The BSI allowed an individual with chronic tetraplegia to stand, walk, climb stairs, and traverse complex terrains, restoring natural control of movement after paralysis. At the end of this thesis, we report for the first time the restoration of communication with a digital bridge between the brain and spinal cord that enabled an individual with chronic tetraplegia to stand and walk naturally.