From spinal central pattern generators to cortical network: integrated BCI for walking rehabilitation.

Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs). Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG), upper limb electromyogram (EMG), or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs) or dynamic recurrent neural networks (DRNNs). Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy.

Oscillations in the human brain during walking execution, imagination and observation.

Gait is an essential human activity which organizes many functional and cognitive behaviors. The biomechanical constraints of bipedalism implicating a permanent control of balance during gait are taken into account by a complex dialog between the cortical, subcortical and spinal networks. This networking is largely based on oscillatory coding, including changes in spectral power and phase-locking of ongoing neural activity in theta, alpha, beta and gamma frequency bands. This coding is specifically modulated in actual gait execution and representation, as well as in contexts of gait observation or imagination. A main challenge in integrative neuroscience oscillatory activity analysis is to disentangle the brain oscillations devoted to gait control. In addition to neuroimaging approaches, which have highlighted the structural components of an extended network, dynamic high-density EEG gives non-invasive access to functioning of this network. Here we revisit the neurophysiological foundations of behavior-related EEG in the light of current neuropsychological theoretic frameworks. We review different EEG rhythms emerging in the most informative paradigms relating to human gait and implications for rehabilitation strategies.