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Most BCI (brain-computer interface) research to date has been based on 4 assumptions that:

  • 1.intended actions are fully represented in the cerebral cortex;
  • 2.neuronal action potentials can provide the best picture of an intended action;
  • 3.the best BCI is one that records action potentials and decodes them; and
  • 4.ongoing mutual adaptation by the BCI user and the BCI system is not very important. In reality, none of these assumptions is presently defensible.

Intended actions are the products of many areas, from the cortex to the spinal cord, and the contributions of each area change continually. BCIs must track and guide these changes if they are to achieve and maintain good performance.

Furthermore, it is not yet clear which category of brain signals will prove most effective for BCI applications. In human studies to date, low-resolution electroencephalography-based BCIs perform as well as high-resolution cortical neuron-based BCIs. In sum, BCIs allow their users to develop new skills in which the users control brain signals rather than muscles. Thus, the central task of BCI research is to determine which brain signals users can best control, to maximize that control, and to translate it accurately and reliably into actions that accomplish the users’ intentions.