Brain-machine interfaces: an overview <s> Extraction of Information from neuronal ensembles <s> Since the original demonstration that electrical activity generated by ensembles of cortical neurons can be employed directly to control a robotic manipulator, research on brain–machine interfaces (BMIs) has experienced an impressive growth. Today BMIs designed for both experimental and clinical studies can translate raw neuronal signals into motor commands that reproduce arm reaching and hand grasping movements in artificial actuators. Clearly, these developments hold promise for the restoration of limb mobility in paralyzed subjects. However, as we review here, before this goal can be reached several bottlenecks have to be passed. These include designing a fully implantable biocompatible recording device, further developing real-time computational algorithms, introducing a method for providing the brain with sensory feedback from the actuators, and designing and building artificial prostheses that can be controlled directly by brain-derived signals. By reaching these milestones, future BMIs will be able to drive and control revolutionary prostheses that feel and act like the human arm. <s> BIB001 </s> Brain-machine interfaces: an overview <s> Extraction of Information from neuronal ensembles <s> The ability to walk may be critically impacted as the result of neurological injury or disease. While recent advances in brain-machine interfaces (BMIs) have demonstrated the feasibility of upper-limb neuroprostheses, BMIs have not been evaluated as a means to restore walking. Here, we demonstrate that chronic recordings from ensembles of cortical neurons can be used to predict the kinematics of bipedal walking in rhesus macaques – both offline and in real-time. Linear decoders extracted 3D coordinates of leg joints and leg muscle EMGs from the activity of hundreds of cortical neurons. As more complex patterns of walking were produced by varying the gait speed and direction, larger neuronal populations were needed to accurately extract walking patterns. Extraction was further improved using a switching decoder which designated a submodel for each walking paradigm. We propose that BMIs may one day allow severely paralyzed patients to walk again. <s> BIB002
The larger the neuronal ensemble recorded, simultaneously BIB002 . Additionally, ensemble recordings assure stability of BMI control BIB001 . As to Jennifer Aniston neurons, they are more likely to be discovered when there is a large neuronal sample to select from.