@article {2193, title = {Decoding flexion of individual fingers using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {6}, year = {2009}, month = {12/2009}, pages = {066001}, abstract = {

Brain signals can provide the basis for a non-muscular communication and control system, a brain-computer interface (BCI), for people with motor disabilities. A common approach to creating BCI devices is to decode kinematic parameters of movements using signals recorded by intracortical microelectrodes. Recent studies have shown that kinematic parameters of hand movements can also be accurately decoded from signals recorded by electrodes placed on the surface of the brain (electrocorticography (ECoG)). In the present study, we extend these results by demonstrating that it is also possible to decode the time course of the flexion of individual fingers using ECoG signals in humans, and by showing that these flexion time courses are highly specific to the moving finger. These results provide additional support for the hypothesis that ECoG could be the basis for powerful clinically practical BCI systems, and also indicate that ECoG is useful for studying cortical dynamics related to motor function.

}, keywords = {Adolescent, Adult, Biomechanics, Brain, Electrodiagnosis, Epilepsy, Female, Fingers, Humans, Male, Microelectrodes, Middle Aged, Motor Activity, Rest, Thumb, Time Factors, Young Adult}, issn = {1741-2552}, doi = {10.1088/1741-2560/6/6/066001}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19794237}, author = {Kub{\'a}nek, J and Miller, John W and Ojemann, J G and Jonathan Wolpaw and Gerwin Schalk} } @article {2178, title = {The emerging world of motor neuroprosthetics: a neurosurgical perspective.}, journal = {Neurosurgery}, volume = {59}, year = {2006}, month = {07/2006}, pages = {1-14; discussion 1-14}, abstract = {

A MOTOR NEUROPROSTHETIC device, or\ brain computer interface, is a machine that can take some type of signal from the\ brain\ and convert that information into overt device control such that it reflects the intentions of the user{\textquoteright}s\ brain. In essence, these constructs can decode the electrophysiological signals representing motor intent. With the parallel evolution of neuroscience, engineering, and rapid computing, the era of clinical neuroprosthetics is approaching as a practical reality for people with severe motor impairment. Patients with such diseases as spinal cord injury, stroke, limb loss, and neuromuscular disorders may benefit through the implantation of these\ brain computer interfaces\ that serve to augment their ability to communicate and interact with their environment. In the upcoming years, it will be important for the neurosurgeon to understand what a\ brain computer interface\ is, its fundamental principle of operation, and what the salient surgical issues are when considering implantation. We review the current state of the field of motor neuroprosthetics research, the early clinical applications, and the essential considerations from a neurosurgical perspective for the future.

}, keywords = {Brain, Humans, Man-Machine Systems, Movement, Neurosurgery, Prostheses and Implants, User-Computer Interface}, issn = {1524-4040}, doi = {10.1227/01.NEU.0000221506.06947.AC}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16823294}, author = {Leuthardt, E C and Gerwin Schalk and Moran, D and Ojemann, J G} } @article {2168, title = {A brain-computer interface using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {1}, year = {2004}, month = {06/2004}, pages = {63-71}, abstract = {

Brain-computer interfaces\ (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the\ brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the\ brain\ can enable users to control a one-dimensional\ computer\ cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100\% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the\ brain.

}, keywords = {Adult, Brain, Communication Aids for Disabled, Computer Peripherals, Diagnosis, Computer-Assisted, Electrodes, Implanted, Electroencephalography, Evoked Potentials, Female, Humans, Imagination, Male, Movement Disorders, User-Computer Interface}, issn = {1741-2560}, doi = {10.1088/1741-2560/1/2/001}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15876624}, author = {Leuthardt, E C and Gerwin Schalk and Jonathan Wolpaw and Ojemann, J G and Moran, D} }