As an advantage of this approach over typical BMIs and once the individual decoders are learnt, subjects do not need to learn to modulate their brain activity in order to generate all necessary movement parameters to operate the neuroprosthesis, which imposes natural limits on the complexity of tasks that can be solved. A BMI can mimic this principle, as studies have shown the feasibility to decode such a kind of cognitive information associated with voluntary goal-directed movements 3, 12, 13. In fact, many elements of skilled movements, from manipulation to walking, are mainly handled at the brainstem and spinal cord level with cortical areas providing an abstraction of the desired movement such as goals and movement onset 11. Mounting evidence, however, seems to suggest that motor control is the result of the combined activity of the cerebral cortex, subcortical areas and spinal cord. This control approach directly links neural activity to motor behaviour 10. These BMIs typically decode cortical correlates of movement parameters (velocity/position 1, 5, 6, 7, 8, 9 or muscular activity 4) in order to generate the sequence of movements for the neuroprosthesis. Research on brain-machine interfaces (BMI) has demonstrated how subjects can voluntary modulate brain signals to operate neuroprosthetic devices 1, 2, 3, 4, 5, 6. Furthermore, our paradigm can seamlessly incorporate other cognitive signals and conventional neuroprosthetic approaches, invasive or non-invasive, to enlarge the range and complexity of tasks that can be accomplished. We anticipate this BMI approach to become a key component of any neuroprosthesis that mimics natural motor control as it enables continuous adaptation in the absence of explicit information about goals. Our results further support that these error-related signals reflect a task-independent monitoring mechanism in the brain, making this teaching paradigm scalable. Results show that, after a short user’s training period, this teaching BMI paradigm operated three different neuroprostheses and generalized across several targets.
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In our approach the neuroprosthesis executes actions that the subject evaluates as erroneous or correct and exploits the brain correlates of this assessment to learn suitable motor behaviours. Here we demonstrate an alternative and complementary BMI paradigm that overcomes that limitation by decoding cognitive brain signals associated with monitoring processes relevant for achieving goals. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the complexity of tasks that can be performed. Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses.