High Precision Neural Decoding of Complex Movement Trajectories using Recursive Bayesian Estimation with Dynamic Movement Primitives.

Brain-machine interfaces (BMIs) are a rapidly progressing technology with the potential to restore function to victims of severe paralysis via neural control of robotic systems. Great strides have been made in directly mapping a user's cortical activity to control of the individual degrees of freedom of robotic end-effectors. While BMIs have yet to achieve the level of reliability desired for widespread clinical use, environmental sensors (e.

Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject.

Objective: We used native sensorimotor representations of fingers in a brain-machine interface (BMI) to achieve immediate online control of individual prosthetic fingers.

Approach: Using high gamma responses recorded with a high-density electrocorticography (ECoG) array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: (1) if any finger was moving, and, if so, (2) which digit was moving.

Neuroprosthetic limb control with electrocorticography: approaches and challenges.

Advanced upper limb prosthetics, such as the Johns Hopkins Applied Physics Lab Modular Prosthetic Limb (MPL), are now available for research and preliminary clinical applications. Research attention has shifted to developing means of controlling these prostheses. Penetrating microelectrode arrays are often used in animal and human models to decode action potentials for cortical control.

Coarse electrocorticographic decoding of ipsilateral reach in patients with brain lesions.

In patients with unilateral upper limb paralysis from strokes and other brain lesions, strategies for functional recovery may eventually include brain-machine interfaces (BMIs) using control signals from residual sensorimotor systems in the damaged hemisphere. When voluntary movements of the contralateral limb are not possible due to brain pathology, initial training of such a BMI may require use of the unaffected ipsilateral limb. We conducted an offline investigation of the feasibility of decoding ipsilateral upper limb movements from electrocorticographic (ECoG) recordings in three patients with different lesions of sensorimotor systems associated with upper limb control.

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.

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas.

Simultaneous neural control of simple reaching and grasping with the modular prosthetic limb using intracranial EEG.

Intracranial electroencephalographic (iEEG) signals from two human subjects were used to achieve simultaneous neural control of reaching and grasping movements with the Johns Hopkins University Applied Physics Lab (JHU/APL) Modular Prosthetic Limb (MPL), a dexterous robotic prosthetic arm. We performed functional mapping of high gamma activity while the subject made reaching and grasping movements to identify task-selective electrodes. Independent, online control of reaching and grasping was then achieved using high gamma activity from a small subset of electrodes with a model trained on short blocks of reaching and grasping with no further adaptation.

Electrocorticographic decoding of ipsilateral reach in the setting of contralateral arm weakness from a cortical lesion.

Brain machine interfaces have the potential for restoring motor function not only in patients with amputations or lesions of efferent pathways in the spinal cord and peripheral nerves, but also patients with acquired brain lesions such as strokes and tumors. In these patients the most efficient components of cortical motor systems are not available for BMI control. Here we had the opportunity to investigate the possibility of utilizing subdural electrocorticographic (ECoG) signals to control natural reaching movements under these circumstances.