Abstract: Disclosed herein is a method for training a subject to produce a new neural activity pattern that results in a desired behavior. The method creates a brain-computer interface mapping between a neural activity pattern of a set of neural units and the desired behavior in an intrinsic manifold, without learning. An outside manifold perturbation of the mapping is then created, defining a new neural activity pattern lying outside of the intrinsic manifold that will produce the desired behavior. The new neural activity pattern is taught by incrementally perturbing the neural activity pattern that produces the desired behavior between the intrinsic manifold and the outside manifold perturbation and having the subject learn the desired behavior for each increment.

Abstract: Artificial control of a prosthetic device is provided. A brain machine interface contains a mapping of neural signals and corresponding intention estimating kinematics (e.g. positions and velocities) of a limb trajectory. The prosthetic device is controlled by the brain machine interface. During the control of the prosthetic device, a modified brain machine interface is developed by modifying the vectors of the velocities defined in the brain machine interface. The modified brain machine interface includes a new mapping of the neural signals and the intention estimating kinematics that can now be used to control the prosthetic device using recorded neural brain signals from a user of the prosthetic device. In one example, the intention estimating kinematics of the original and modified brain machine interface includes a Kalman filter modeling velocities as intentions and positions as feedback.

Abstract: Artificial control of a prosthetic device is provided. A brain machine interface contains a mapping of neural signals and corresponding intention estimating kinematics (e.g. positions and velocities) of a limb trajectory. The prosthetic device is controlled by the brain machine interface. During the control of the prosthetic device, a modified brain machine interface is developed by modifying the vectors of the velocities defined in the brain machine interface. The modified brain machine interface includes a new mapping of the neural signals and the intention estimating kinematics that can now be used to control the prosthetic device using recorded neural brain signals from a user of the prosthetic device. In one example, the intention estimating kinematics of the original and modified brain machine interface includes a Kalman filter modeling velocities as intentions and positions as feedback.

Abstract: A brain machine interface for decoding neural signals for control of a machine is provided. The brain machine interface estimates and then combines information from two classes of neural activity. A first estimator decodes movement plan information from neural signals representing plan activity. In one embodiment the first estimator includes an adaptive point-process filter or a maximum likelihood filter. A second estimator decodes peri-movement information from neural signals representing peri-movement activity. Each estimator is designed to estimate different aspects of movement such as movement goal variables or movement execution variables.

Abstract: A contactless conductivity detector uses a signal electrode arranged longitudinally between two ground electrodes. Conveniently, the ground electrodes can be integral with a metal housing that electrically shields the detector electronics from external electrical noise. The ground electrodes are defined at the locations where a capillary separation channel extends through the housing. The signal electrode is coupled to an AC transmitter through a sense resistor; the signal electrode is also coupled to a receiver that provides an output corresponding to the magnitude of the AC signal developed at the signal electrode. The signal electrode is located at a voltage-divider node between the sense resistor and the resistances of the sample fluid between the signal electrode location and the ground electrode locations. Thus, the signal developed at the signal electrode reflects the sample fluid resistance, which is the inverse of its conductivity.