Walking naturally after spinal cord injury using a brain-spine interface.

A spinal cord injury interrupts the communication between the brain and the region of the spinal cord that produces walking, leading to paralysis. Here, we restored this communication with a digital bridge between the brain and spinal cord that enabled an individual with chronic tetraplegia to stand and walk naturally in community settings. This brain-spine interface (BSI) consists of fully implanted recording and stimulation systems that establish a direct link between cortical signals and the analogue modulation of epidural electrical stimulation targeting the spinal cord regions involved in the production of walking.

An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic.

The article aims at addressing 2 challenges to step motor brain-computer interface (BCI) out of laboratories: asynchronous control of complex bimanual effectors with large numbers of degrees of freedom, using chronic and safe recorders, and the decoding performance stability over time without frequent decoder recalibration.Closed-loop adaptive/incremental decoder training is one strategy to create a model stable over time. Adaptive decoders update their parameters with new incoming data, optimizing the model parameters in real time.

Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients.

The evaluation of the long-term stability of ElectroCorticoGram (ECoG) signals is an important scientific question as new implantable recording devices can be used for medical purposes such as Brain-Computer Interface (BCI) or brain monitoring.The long-term clinical validation of wireless implantable multi-channel acquisition system for generic interface with neurons (WIMAGINE), a wireless 64-channel epidural ECoG recorder was investigated. The WIMAGINE device was implanted in two quadriplegic patients within the context of a BCI protocol.

The Riemannian spatial pattern method: mapping and clustering movement imagery using Riemannian geometry.

. Over the last decade, Riemannian geometry has shown promising results for motor imagery classification. However, extracting the underlying spatial features is not as straightforward as for applying common spatial pattern (CSP) filtering prior to classification.