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Brain-Computer Interfaces

& Neuroprostheses
Electrocorticography (ECoG)
 Abstract
Electrocorticography (ECoG) is a neural signal recording modality that measures electrical potentials using a grid of electrodes placed on the surface of the cortex. ECoG provides information about a user's neural signals with higher spatial resolution and bandwidth than EEG and over wider cortical areas with more robustness to small changes in recording site location than microelectrode arrays (MEAs). Since ECoG electrodes do not penetrate the cortex, recorded signals are also not subjected as heavily to immune response. ECoG therefore occupies a unique niche as a potential long-term BCI technolog. Our lab is studying the spatiotemporal patterns of ECoG activity that are correlated with movements of the upper limb, hand, and fingers. We can use our knowledge of the robust patterns in neural activity (e.g., event-related synchronization, ERS, and desynchronization, ERD) preceding complex movements for continuous human control of virtual and physical neuroprosthetics.

 
Motor Prosthesis
 
ECoG coverage over motor areas allows us to identify and decode the neural correlates of reaching and grasping movements. These can be mapped in real-time to corresponding movements by the modular prosthetic limb (MPL) developed by the applied physics laboratory. We have developed the first ECoG BMI to give simultaneous, independent control of a robotic limb without the need for operant conditioning seen here . Furthermore, we have augmented patients' neural control over the MPL with computer vision to allow patients to manipulate objects with minimal cognitive burden seen here .
 
Diagram of a Hybrid ECoG and Eye-Tracking Controlled Semi-Autonomous BMI. IEEE TNSRE
 
Speech Prosthesis
 
ECoG presents a unique opportunity to study speech and language in human subjects with an unparalleled degree of signal quality, coverage, and spatiotemporal resolution. Whereas most bioelectric research of speech and language is performed in animal models with varying degrees of overlap with human language, our collaboration with Dr. Nathan Crone at the Johns Hopkins Hospital allows us to perform intricate speech/language paradigms with patients undergoing neurosurgical intervention for intractable epilepsy. We are currently researching more efficient ways of mapping eloquent speech cortex using connectivity metrics in order to eliminate unexpected speech/language deficits caused by resection of epileptogenic tissue. This research will see further application in online speech neuroprostheses for restoration of speech in profoundly disabled individuals.
 
Researchers
Nathan Crone, MD (Collaborator)
Brock Wester, PhD (Collaborator)
Dana Boatman, PhD (Collaborator)
Geoffrey Newman, BS
Guy Hotson, BS
Griffin Milsap, BS
Yujing Wang, MS
Matthew Fifer, BS
 
Collaborating Institutions
Epilepsy Monitoring Unit, Johns Hopkins Neurology
Johns Hopkins University Applied Physics Laboratory
 
Funding
National Institutes of Health, Grant 3R01NS040596-09A2S1
 
Publications

McMullen D*, Hotson G*, Katyal KD, Wester BA, Fifer MS, McGee T, Harris A, Johannes MS, Vogelstein RJ, Ravitz A, Anderson WS, Thakor NV, and Crone NE, Demonstration of a Semi-Autonomous Hybrid Brain-Machine Interface using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic IEEE Transactions on Neural Systems and Rehabilitation Engineering 2014.

Fifer MS*, Hotson G*, Wester BA, McMullen DP, Wang Y, Johannes MS, Katyal KD, Helder JB, Para MP, Vogelstein RJ, Anderson WS, Thakor NV, and Crone NE, Simultaneous Neural Control of Simple Reaching and Grasping with the Modular Prosthetic Limb using Intracranial EEG IEEE Transactions on Neural Systems and Rehabilitation Engineering 2013.

Benz HL, Zhang H, Bezerianos A, Acharya S, Crone NE, Zheng X, Thakor NV, Connectivity analysis as a novel approach to motor decoding for prosthesis control, IEEE Trans Neural Syst Rehabil Eng, 20(2):143-52, 2012

Fifer MS, Mollazadeh M, Acharya S, Thakor NV, Crone NE, Asynchronous Decoding of Grasp Aperture from Human ECoG During a Reach-to-Grap Task, Conf Proc IEEE Eng Med Biol Soc, 2011:4584-7, 2011

Acharya S, Fifer MS, Benz HL, Crone NE, Thakor NV,Electrocorticographic amplitude predicts finger positions during slow grasping motions of the hand, J Neural Eng, 7(4):046002, 2010

Zhang H, Benz HL, Bezerianos A, Acharya S, Crone NE, Maybhate A, Zheng X, Thakor NV, Connectivity mapping of the human ECoG during a motor task with a time-varying dynamic Bayesian network, Conf Proc IEEE Eng Med Biol Soc, 2010:130-3, 2010

 
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