%0 Journal Article %J Proc Natl Acad Sci U S A %D 2010 %T Cortical activity during motor execution, motor imagery, and imagery-based online feedback. %A Miller, K.J. %A Gerwin Schalk %A Fetz, Eberhard E %A den Nijs, Marcel %A Ojemann, J G %A Rao, Rajesh P N %K Adolescent %K Adult %K Biofeedback, Psychology %K Cerebral Cortex %K Child %K Electric Stimulation %K Electrocardiography %K Female %K Humans %K Male %K Middle Aged %K Motor Activity %K Young Adult %X

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 motor imagery-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 motor imagery 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 cortical activity 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.

%B Proc Natl Acad Sci U S A %V 107 %P 4430-5 %8 03/2010 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/20160084 %N 9 %R 10.1073/pnas.0913697107 %0 Journal Article %J J Neural Eng %D 2009 %T Decoding flexion of individual fingers using electrocorticographic signals in humans. %A Kubánek, J %A Miller, John W %A Ojemann, J G %A Jonathan Wolpaw %A Gerwin Schalk %K Adolescent %K Adult %K Biomechanics %K Brain %K Electrodiagnosis %K Epilepsy %K Female %K Fingers %K Humans %K Male %K Microelectrodes %K Middle Aged %K Motor Activity %K Rest %K Thumb %K Time Factors %K Young Adult %X

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.

%B J Neural Eng %V 6 %P 066001 %8 12/2009 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/19794237 %N 6 %R 10.1088/1741-2560/6/6/066001 %0 Journal Article %J Neural Netw %D 2009 %T Mapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features. %A Gunduz, Aysegul %A Sanchez, Justin C %A Carney, Paul R %A Principe, Jose %K Algorithms %K Brain %K Brain Mapping %K Electrodes, Implanted %K Electrodiagnosis %K Epilepsy %K Feasibility Studies %K Hand %K Humans %K Linear Models %K Motor Activity %K Neural Networks (Computer) %K Nonlinear Dynamics %K Signal Processing, Computer-Assisted %X

Brain-machine interfaces (BMIs) aim to translate the motor intent of locked-in patients into neuroprosthetic control commands. Electrocorticographical (ECoG) signals provide promising neural inputs to BMIs as shown in recent studies. In this paper, we utilize a broadband spectrum above the fast gamma ranges and systematically study the role of spectral resolution, in which the broadband is partitioned, on the reconstruction of the patients' hand trajectories. Traditionally, the power of ECoG rhythms (<200-300 Hz) has been computed in short duration bins and instantaneously and linearly mapped to cursor trajectories. Neither time embedding, nor nonlinear mappings have been previously implemented in ECoG neuroprosthesis. Herein, mapping of neural modulations to goal-oriented motor behavior is achieved via linear adaptive filters with embedded memory depths and as a novelty through echo state networks (ESNs), which provide nonlinear mappings without compromising training complexity or increasing the number of model parameters, with up to 85% correlation. Reconstructed hand trajectories are analyzed through spatial, spectral and temporal sensitivities. The superiority of nonlinear mappings in the cases of low spectral resolution and abundance of interictal activity is discussed.

%B Neural Netw %V 22 %P 1257-70 %8 11/2009 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/19647981 %N 9 %R 10.1016/j.neunet.2009.06.036 %0 Journal Article %J Epilepsy Behav %D 2008 %T Voluntary brain regulation and communication with electrocorticogram signals. %A Hinterberger, T. %A Widman, Guido %A Lal, T.N %A Jeremy Jeremy Hill %A Tangermann, Michael %A Rosenstiel, W. %A Schölkopf, B %A Elger, Christian %A Niels Birbaumer %K Adult %K Biofeedback, Psychology %K Cerebral Cortex %K Communication Aids for Disabled %K Dominance, Cerebral %K Electroencephalography %K Epilepsies, Partial %K Female %K Humans %K Imagination %K Male %K Middle Aged %K Motor Activity %K Motor Cortex %K Signal Processing, Computer-Assisted %K Software %K Somatosensory Cortex %K Theta Rhythm %K User-Computer Interface %K Writing %X

Brain-computer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain signals such as the electroencephalogram (EEG). Three of five patients with epilepsy were able to spell their names with electrocorticogram (ECoG) signals derived from motor-related areas within only one or two training sessions. Imagery of finger or tongue movements was classified with support-vector classification of autoregressive coefficients derived from the ECoG signals. After training of the classifier, binary classification responses were used to select letters from a computer-generated menu. Offline analysis showed increased theta activity in the unsuccessful patients, whereas the successful patients exhibited dominant sensorimotor rhythms that they could control. The high spatial resolution and increased signal-to-noise ratio in ECoG signals, combined with short training periods, may offer an alternative for communication in complete paralysis, locked-in syndrome, and motor restoration.

%B Epilepsy Behav %V 13 %P 300-6 %8 08/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/18495541 %N 2 %R 10.1016/j.yebeh.2008.03.014