A brain-computer interface (BCI) is a system that allows users, especially individuals with severe neuromuscular disorders, to communicate and control devices using their brain waves. There are over two million people in the United States afflicted by such disorders, many of whom could greatly benefit from assistive devices controlled by a BCI. Over the past two years, it has been demonstrated that a non-invasive, scalp-recorded electroencephalography (EEG) based BCI paradigm can be used by a disabled individual for long-term, reliable control of a personal computer. This BCI paradigm allows users to select from a set of symbols presented in a flashing visual matrix by classifying the resulting evoked brain responses. One of the goals of this project is to establish that the same BCI paradigm and techniques used for the aforementioned demonstration can be straightforwardly implemented to generate high-level commands for controlling a robotic manipulator in three dimensions according to user intent, and that such a BCI can provide superior dimensional control over alternative BCI techniques currently available, as well as a wider variety of practical functions for performing everyday tasks.

Electrocorticography (ECoG), electrical activity recorded directly from the surface of the brain, has been demonstrated in recent preliminary work to be another potentially viable control for a BCI. ECoG has been shown to have superior signal-to-noise ratio, and spatial and spectral characteristics, compared to EEG. But the EEG signals used at present to operate BCIs have not been characterized in ECoG. The PI believes ECoG signals can be used to improve the speed and accuracy of BCI applications, including for example control of a robotic manipulator. Thus, additional goals of this project are to characterize evoked responses obtained from ECoG, to use them as control signals to operate a simulated robotic manipulator, and to assess the level of control (speed and accuracy) between the two recording modalities and compare the results to competitive BCI techniques. Because this is a collaborative effort with the Departments of Neurology and Neurosurgery at the Mayo Clinic in Jacksonville, the PI team will have access to a pool of ECoG grid patients from which to recruit participants for this study.

Broader Impacts: This research will make a number of contributions in the emerging field of BCI and thus will serve as a step toward providing severely disabled individuals with a new level of autonomy for communicating with others and for performing everyday tasks, which will ultimately dramatically improve their quality of life.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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Ephraim P. Glinert
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University of North Florida
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