@article {2199, title = {Cortical activity during motor execution, motor imagery, and imagery-based online feedback.}, journal = {Proc Natl Acad Sci U S A}, volume = {107}, year = {2010}, month = {03/2010}, pages = {4430-5}, abstract = {

Imagery\ of\ motor\ movement plays an important role in learning of complex\ motor\ skills, from learning to serve in tennis to perfecting a pirouette in ballet. What and where are the neural substrates that underlie\ motorimagery-based\ learning? We measured electrocorticographic\ cortical\ surface potentials in eight human subjects during overt action and kinesthetic\ imagery\ of the same movement, focusing on power in "high frequency" (76-100 Hz) and "low frequency" (8-32 Hz) ranges. We quantitatively establish that the spatial distribution of local neuronal population\ activity\ during\ motorimagery\ mimics the spatial distribution of\ activity\ during actual\ motor\ movement. By comparing responses to electrocortical stimulation with\ imagery-induced\ cortical\ surface\ activity, we demonstrate the role of primary\ motor\ areas in movement\ imagery. The magnitude of\ imagery-induced\ corticalactivity\ change was approximately 25\% of that associated with actual movement. However, when subjects learned to use this\ imagery\ to control a computer cursor in a simple\ feedback\ task, the\ imagery-induced\ activity\ change was significantly augmented, even exceeding that of overt movement.

}, keywords = {Adolescent, Adult, Biofeedback, Psychology, Cerebral Cortex, Child, Electric Stimulation, Electrocardiography, Female, Humans, Male, Middle Aged, Motor Activity, Young Adult}, issn = {1091-6490}, doi = {10.1073/pnas.0913697107}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20160084}, author = {Miller, K.J. and Gerwin Schalk and Fetz, Eberhard E and den Nijs, Marcel and Ojemann, J G and Rao, Rajesh P N} } @article {2182, title = {Decoding two-dimensional movement trajectories using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {4}, year = {2007}, month = {09/2007}, pages = {264-75}, abstract = {

Signals from the brain could provide a non-muscular communication and control system, a brain-computer interface (BCI), for people who are severely paralyzed. A common BCI research strategy begins by decoding kinematic parameters from brain signals recorded during actual arm movement. It has been assumed that these parameters can be derived accurately only from signals recorded by intracortical microelectrodes, but the long-term stability of such electrodes is uncertain. The present study disproves this widespread assumption by showing in humans that kinematic parameters can also be decoded from signals recorded by subdural electrodes on the cortical surface (ECoG) with an accuracy comparable to that achieved in monkey studies using intracortical microelectrodes. A new ECoG feature labeled the local motor potential (LMP) provided the most information about movement. Furthermore, features displayed cosine tuning that has previously been described only for signals recorded within the brain. These results suggest that ECoG could be a more stable and less invasive alternative to intracortical electrodes for BCI systems, and could also prove useful in studies of motor function.

}, keywords = {Adult, Algorithms, Arm, Brain Mapping, Cerebral Cortex, Electroencephalography, Evoked Potentials, Motor, Female, Humans, Male, Movement}, issn = {1741-2560}, doi = {10.1088/1741-2560/4/3/012}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17873429}, author = {Gerwin Schalk and Kub{\'a}nek, J and Miller, John W and Nicholas R Anderson and Leuthardt, E C and Ojemann, J G and Limbrick, D and Moran, D and Lester A Gerhardt and Jonathan Wolpaw} } @article {2173, title = {Electrocorticography-based brain computer interface--the Seattle experience.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {194-8}, abstract = {

Electrocorticography (ECoG) has been demonstrated to be an effective modality as a platform for brain-computer interfaces (BCIs). Through our experience with ten subjects, we further demonstrate evidence to support the power and flexibility of this signal for BCI usage. In a subset of four patients, closed-loop BCI experiments were attempted with the patient receiving online feedback that consisted of one-dimensional cursor movement controlled by ECoG features that had shown correlation with various real and imagined motor and speech tasks. All four achieved control, with final target accuracies between 73\%-100\%. We assess the methods for achieving control and the manner in which enhancing online control can be accomplished by rescreening during online tasks. Additionally, we assess the relevant issues of the current experimental paradigm in light of their clinical constraints.

}, keywords = {Cerebral Cortex, Electroencephalography, Epilepsy, Evoked Potentials, Humans, Therapy, Computer-Assisted, User-Computer Interface, Washington}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875536}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792292}, author = {Leuthardt, E C and Miller, John W and Gerwin Schalk and Rao, Rajesh P N and Ojemann, J G} }