Development of Brain-machine Interfaces

A brain-machine interface (BMI) uses signals from the brain to control electronic devices. One application of this technology is the control of assistive devices to facilitate movement after paralysis. Ideally, the BMI would identify an intended movement and control an assistive device to produce the desired movement. To implement such a system, it is necessary to identify different movements involving a single limb and users must be able to issue commands at any instant instead of only during specific time windows determined by the BMI itself.\n\nA novel processing technique to identify voluntary movements using only four electrodes is presented. Histograms containing the spectral components of intracranial neural signals displaying power changes correlated with movement were unique for each of three movements performed with one limb. Off-line classification of the histograms allowed the identification of the performed movement with an accuracy of 89%. \n\nThis movement identification system was interfaced with a neuroprosthesis for grasping, fitted to a tetraplegic individual. The user pressed a button triggering the random selection and classification of a brain signal previously recorded intracranially from a different person while performing specific arm movements. Correct identification of the movement triggered grasping functions. Movement identification accuracy was 94% allowing successful operation of the neuroprosthesis.\n\nFinally, two BMIs for the real-time asynchronous control of two-dimensional movements were created using a single electrode. One EEG-based system was tested by a healthy participant. A second system was implemented and tested using recordings from an individual undergoing clinical intracranial electrode implantation. The users modulated their 7 Hz-13 Hz oscillatory rhythm through motor imagery. A power decrease below a threshold activated a ``brain-switch''. This switch was coupled with a novel asynchronous control strategy to control a miniature remotely-controlled vehicle as well as a computer cursor. Successful operation of the EEG system required 6 hrs of training. ECoG control was achieved after only 15 minutes. The operation of the BMI was simple enough to allow users to focus on the task at hand rather than on the actual operation of the BMI.

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