The current state of electrocorticography-based brain-computer interfaces.

Brain-computer interfaces (BCIs) provide a way for the brain to interface directly with a computer. Many different brain signals can be used to control a device, varying in ease of recording, reliability, stability, temporal and spatial resolution, and noise. Electrocorticography (ECoG) electrodes provide a highly reliable signal from the human brain surface, and these signals have been used to decode movements, vision, and speech. ECoG-based BCIs are being developed to provide increased options for treatment and assistive devices for patients who have functional limitations. Decoding ECoG signals in real time provides direct feedback to the patient and can be used to control a cursor on a computer or an exoskeleton. In this review, the authors describe the current state of ECoG-based BCIs that are approaching clinical viability for restoring lost communication and motor function in patients with amyotrophic lateral sclerosis or tetraplegia. These studies provide a proof of principle and the possibility that ECoG-based BCI technology may also be useful in the future for assisting in the cortical rehabilitation of patients who have suffered a stroke.

[1]  R. Lesser,et al.  Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. I. Alpha and beta event-related desynchronization. , 1998, Brain : a journal of neurology.

[2]  Christopher Kennard,et al.  Oculomotor dysfunction in amyotrophic lateral sclerosis: a comprehensive review. , 2011, Archives of neurology.

[3]  Byron M. Yu,et al.  A high-performance brain–computer interface , 2006, Nature.

[4]  Pascal Auquier,et al.  Quality of life in patients with locked-in syndrome: Evolution over a 6-year period , 2015, Orphanet Journal of Rare Diseases.

[5]  Edward F. Chang,et al.  Speech synthesis from neural decoding of spoken sentences , 2019, Nature.

[6]  N E Crone.  Functional mapping with ECoG spectral analysis. , 2000, Advances in neurology.

[7]  Nathan P. Staff,et al.  Amyotrophic Lateral Sclerosis: An Update for 2018 , 2018, Mayo Clinic proceedings.

[8]  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.

[9]  Kristofer E. Bouchard,et al.  Functional Organization of Human Sensorimotor Cortex for Speech Articulation , 2013, Nature.

[10]  Anton Nijholt,et al.  Brain-Computer Interfaces Handbook: Technological and Theoretical Advances , 2018 .

[11]  K. Miller,et al.  A Simple, Spectral-Change Based, Electrocorticographic Brain–Computer Interface , 2009 .

[12]  R. Lesser,et al.  Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band. , 1998, Brain : a journal of neurology.

[13]  N. Ramsey,et al.  Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS. , 2016, The New England journal of medicine.

[14]  Andreas Gohritz,et al.  Tetraplegia Management Update. , 2015, The Journal of hand surgery.

[15]  Kai J Miller,et al.  A library of human electrocorticographic data and analyses , 2019, Nature Human Behaviour.

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

[17]  Joshua A. Salomon,et al.  GLOBAL, REGIONAL, AND COUNTRY-SPECIFIC LIFETIME RISK OF STROKE, 1990–2016 , 2018, The New England journal of medicine.

[18]  Tonio Ball,et al.  Mapping the fine structure of cortical activity with different micro-ECoG electrode array geometries , 2017, Journal of neural engineering.

[19]  Salvador Pané,et al.  Tissue Response to Neural Implants: The Use of Model Systems Toward New Design Solutions of Implantable Microelectrodes , 2019, Front. Neurosci..

[20]  Bradley Greger,et al.  Decoding spoken words using local field potentials recorded from the cortical surface , 2010, Journal of neural engineering.

[21]  Timothy Denison,et al.  Stability of a chronic implanted brain-computer interface in late-stage amyotrophic lateral sclerosis , 2019, Clinical Neurophysiology.

[22]  Richard A. Andersen,et al.  Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex , 2019, Neuron.

[23]  Andrew J. Shoffstall,et al.  Microelectrode implantation in motor cortex causes fine motor deficit: Implications on potential considerations to Brain Computer Interfacing and Human Augmentation , 2017, Scientific Reports.

[24]  Franco Pestilli,et al.  Face percept formation in human ventral temporal cortex. , 2017, Journal of neurophysiology.

[25]  Rajesh P. N. Rao,et al.  Localization and classification of phonemes using high spatial resolution electrocorticography (ECoG) grids , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  Rajesh P. N. Rao,et al.  Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change , 2016, PLoS Comput. Biol..

[27]  F. Attneave,et al.  The Organization of Behavior: A Neuropsychological Theory , 1949 .

[28]  Timothy Denison,et al.  Sensorimotor ECoG Signal Features for BCI Control: A Comparison Between People With Locked-In Syndrome and Able-Bodied Controls , 2019, Front. Neurosci..

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

[30]  Francis R. Willett,et al.  High performance communication by people with paralysis using an intracortical brain-computer interface , 2017, eLife.

[31]  Rajesh P. N. Rao,et al.  Broadband changes in the cortical surface potential track activation of functionally diverse neuronal populations , 2014, NeuroImage.

[32]  H. Jasper,et al.  Electrocorticograms in man: Effect of voluntary movement upon the electrical activity of the precentral gyrus , 1949 .

[33]  A. Benabid,et al.  An exoskeleton controlled by an epidural wireless brain–machine interface in a tetraplegic patient: a proof-of-concept demonstration , 2019, The Lancet Neurology.

[34]  D J Weber,et al.  Human motor cortical activity recorded with Micro-ECoG electrodes, during individual finger movements , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[35]  Nick F. Ramsey,et al.  BOLD matches neuronal activity at the mm scale: A combined 7T fMRI and ECoG study in human sensorimotor cortex , 2014, NeuroImage.

[36]  Geon Hwee Kim,et al.  Recent Progress on Microelectrodes in Neural Interfaces , 2018, Materials.

[37]  Kai J Miller,et al.  Reorganization of large-scale physiology in hand motor cortex following hemispheric stroke , 2011, Neurology.

[38]  A. Lambert,et al.  Long-Term Sheep Implantation of WIMAGINE®, a Wireless 64-Channel Electrocorticogram Recorder , 2019, Front. Neurosci..

[39]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[40]  Nick F. Ramsey,et al.  Human Motor Cortical Activity Is Selectively Phase-Entrained on Underlying Rhythms , 2012, PLoS Comput. Biol..

[41]  B. Woods,et al.  Mirror movements after childhood hemiparesis , 1978, Neurology.

[42]  Rajesh P. N. Rao,et al.  Spectral Changes in Cortical Surface Potentials during Motor Movement , 2007, The Journal of Neuroscience.