Brain-machine interface: closer to therapeutic reality?

Various neurological diseases and traumatic injuries permanently abolish sensorimotor functions, dramatically aff ecting the quality of life of millions of individuals. Progress continues in the development of neural repair interventions to enhance functional recovery after neuromotor disorders in animals. So far, no interventions have shown effi cacy in the restoration of useful sensorimotor functions in severely paralysed people. Two decades ago, developments opened avenues for the design of neuroprosthetic systems capable of replacing lost sensorimotor functions. The proliferation of high-density neuronal recordings together with high-performance computing capabilities allowed the identifi cation of core principles through which the brain can coordinate upper limb movements. 3 This knowledge translated into pioneer brain–machine interfaces whereby non-human primates could learn to operate external actuators such as prosthetic arms using only brain activity. The results of the BrainGate 6,7 clinical trial provided the fi rst proof of principle that the ideas developed in non-human primates were transferable to people. However, several technical and practical challenges have delayed clinical implementation. Human brain–machine interfaces have not reached the level of performance attained in non-human primates. In The Lancet, Jennifer Collinger and colleagues 8 report the results of an extensive clinical study in a 52-year-old woman with chronic tetraplegia who performed a range of activities of daily living using a brain–machine-interface-controlled robotic arm. For the fi rst time, a person performed better than non-human primates. Two arrays of 96-channel intracortical microelectrodes were surgically implanted in the motor cortex, allowing recordings of up to 271 single units during the 3-month trial. As early as the second day of testing, the paralysed woman was capable of manipulating the prosthetic limb in a three-dimen sional workspace. Perfor mances and control complexity gradually improved with daily brain– machine-interface training over 13 weeks. Eventually, the participant developed fl uid and rapid control over skilful prosthetic arm movements. She accurately reached for objects, adjusted the opening of the prosthetic hand to grasp items of various shapes and sizes, and moved them to any desired location in the three-dimensional workspace (fi gure). Collinger and colleagues also sought to assess the clinical relevance of regained movements using the action research arm test (ARAT), which is used to grade upper-limb function recovery after neuromotor disorders, but has never been used for the performance assessment of brain–machine interfaces. Signifi cant gains in clinical scores emphasised the value of the developed brain–machine-interface technology for im proving the quality of …