Abstract: Characteristics of biological substances, such as cerebral cortex matter, are sensed. According to an example embodiment, the present invention is directed to a negligibly-intrusive, multi-layer integrated circuit arrangement for monitoring activity of an area of a cerebral cortex that would normally be covered by an anatomical layer. The multi-layer integrated circuit arrangement includes an optics layer located outside the cerebral cortex area that includes an emitter and a detector. The optics layer is adapted for implantation in the anatomical layer and for sensing at least one brain-activity parameter. The multi-layered integrated circuit arrangement also includes a data-processing layer that includes a digital-processing circuit that is adapted for assimilating neural data in response to the optics layer sensing at least one brain-activity parameter.

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 prosthetic system may use a decoder to predict an intended action, such as a reach, from processed signals generated from measured neural activity. The decoder may included a cognitive state machine, which transitions between cognitive states based on transition rules.

Abstract: The present invention provides a processed neural signal that encodes a reach plan, comprising the target location of the planned reach encoded relative to an eye-centered reference frame. The present invention also provides methods for generating and decoding the processed neural signal.

Abstract: The present invention provides a processed neural signal that encodes a reach plan, comprising the target location of the planned encoded relative to an eye-centered reference frame. The present invention also provides methods for generating and decoding the processed neural signal.

Abstract: The present invention provides a processed neural signal that encodes a reach plan, comprising the target location of the planned reach encoded relative to an eye-centered reference frame. The present invention also provides methods for generating and decoding the processed neural signal.

Abstract: A prosthetic system may use a decoder to predict an intended action, such as a reach, from processed signals generated from measured neural activity. The decoder may included a cognitive state machine, which transitions between cognitive states based on transition rules.