Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface.

Human subjects can learn to control a one-dimensional electrocorticographic (ECoG) brain-computer interface (BCI) using modulation of primary motor (M1) high-gamma activity (signal power in the 75-200 Hz range). However, the stability and dynamics of the signals over the course of new BCI skill acquisition have not been investigated. In this study, we report 3 characteristic periods in evolution of the high-gamma control signal during BCI training: initial, low task accuracy with corresponding low power modulation in the gamma spectrum, followed by a second period of improved task accuracy with increasing average power separation between activity and rest, and a final period of high task accuracy with stable (or decreasing) power separation and decreasing trial-to-trial variance. These findings may have implications in the design and implementation of BCI control algorithms.

[1]  Jonathan R Wolpaw,et al.  EEG-Based Communication and Control: Speed–Accuracy Relationships , 2003, Applied psychophysiology and biofeedback.

[2]  Wolfgang Rosenstiel,et al.  An MEG-based brain–computer interface (BCI) , 2007, NeuroImage.

[3]  Paul J. Reber,et al.  Neural correlates of skill acquisition: Decreased cortical activity during a serial interception sequence learning task , 2011, NeuroImage.

[4]  Wolfgang Grodd,et al.  Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) , 2004, IEEE Transactions on Biomedical Engineering.

[5]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  G. Pfurtscheller,et al.  Conversion of EEG activity into cursor movement by a brain-computer interface (BCI) , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  Rajesh P. N. Rao.  Brain-Computer Interfacing: An Introduction , 2010 .

[8]  G. Schalk,et al.  Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans , 2011, Journal of neural engineering.

[9]  Rajesh P. N. Rao,et al.  Electrocorticography-based brain computer Interface-the seattle experience , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Rajesh P. N. Rao,et al.  Brain–computer interfaces: a powerful tool for scientific inquiry , 2014, Current Opinion in Neurobiology.

[11]  Nicholas P. Szrama,et al.  Using the electrocorticographic speech network to control a brain–computer interface in humans , 2011, Journal of neural engineering.

[12]  N.V. Thakor,et al.  Towards a Brain-Computer Interface for Dexterous Control of a Multi-Fingered Prosthetic Hand , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[13]  Gerwin Schalk,et al.  Rapid Communication with a “P300” Matrix Speller Using Electrocorticographic Signals (ECoG) , 2010, Front. Neurosci..

[14]  G. Pfurtscheller,et al.  ‘Thought’ – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia , 2003, Neuroscience Letters.

[15]  Soo-Young Lee,et al.  Brain–computer interface using fMRI: spatial navigation by thoughts , 2004, Neuroreport.

[16]  Gerwin Schalk,et al.  A brain–computer interface using electrocorticographic signals in humans , 2004, Journal of neural engineering.

[17]  P R Kennedy,et al.  Direct control of a computer from the human central nervous system. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[18]  Rajesh P. N. Rao,et al.  Robust, long-term control of an electrocorticographic brain-computer interface with fixed parameters. , 2009, Neurosurgical focus.

[19]  Xenophon Papademetris,et al.  BioImage Suite: An integrated medical image analysis suite: An update. , 2006, The insight journal.

[20]  M. Nicolelis,et al.  Cortical Modulations Increase in Early Sessions with Brain-Machine Interface , 2007, PloS one.

[21]  Dennis J. McFarland,et al.  Design and operation of an EEG-based brain-computer interface with digital signal processing technology , 1997 .

[22]  Rajesh P. N. Rao.  Brain-Computer Interfacing: Major Types of BCIs , 2013 .

[23]  Rajesh P. N. Rao,et al.  Distributed cortical adaptation during learning of a brain–computer interface task , 2013, Proceedings of the National Academy of Sciences.

[24]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[25]  Scott T. Grafton,et al.  Functional Mapping of Sequence Learning in Normal Humans , 1995, Journal of Cognitive Neuroscience.

[26]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[27]  Leslie G. Ungerleider,et al.  The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[29]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[30]  Nick F. Ramsey,et al.  Automated electrocorticographic electrode localization on individually rendered brain surfaces , 2010, Journal of Neuroscience Methods.

[31]  Jeffrey G. Ojemann,et al.  Power-Law Scaling in the Brain Surface Electric Potential , 2009, PLoS Comput. Biol..

[32]  J. A. Wilson,et al.  Two-dimensional movement control using electrocorticographic signals in humans , 2008, Journal of neural engineering.

[33]  A. Schwartz,et al.  Work toward real-time control of a cortical neural prothesis. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[34]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[35]  D.J. McFarland,et al.  The Wadsworth Center brain-computer interface (BCI) research and development program , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[36]  Andrew B. Schwartz,et al.  Brain-Controlled Interfaces: Movement Restoration with Neural Prosthetics , 2006, Neuron.

[37]  Rajesh P. N. Rao,et al.  Cortical activity during motor execution, motor imagery, and imagery-based online feedback , 2010, Proceedings of the National Academy of Sciences.