Neurological signal decoding
A system using neurological control signals to control a device is disclosed. The system may include a sensor sensing electrical activity of a plurality of neurons over time and a vector generator generating a neural control vector from the sensed electrical activity of the plurality of neurons over time. The system may also include a control filter to which the neural control vector is applied to provide a control variable and an output device controlled by the control variable.
1. A method for controlling a device comprising:
providing a sensor sensing electrical activity of a plurality of neurons over time;
generating a neural control vector by storing the sensed electrical activity of the plurality of neurons at each of successive time bins;
providing a control filter;
calculating an innerproduct between the neural control vector and the control filter to provide a control variable; and
controlling an output device with the control variable.
2. The method of claim 1 wherein the sensor comprises an array of electrical sensing elements, and the neural control vector represents the sensed electrical activity at each of the electrical sensing elements at each of the successive time bins.
3. The method of claim 1 wherein the control filter for calculation of a control variable provides a least mean square error between an output of the output device and an intended output.
4. The method of claim 1 wherein the electrical activity of neurons is sensed over 1 to 1000 time bins.
5. The method of claim 1 wherein each of the time bins is 1 to 1000 ms.
6. The method of claim 1 wherein an application of the neural control vector to the control filter results in an innerproduct.
7. The method of claim 1 wherein providing a control filter comprises generating a control filter based on a previously sensed electrical activity and kinematic data concurrently recorded with the previously sensed electrical activity.
8. A method of generating a control filter comprising:
providing a sensor sensing electrical activity of a plurality of neurons over time;
generating a neural control vector by storing the sensed electrical activity of the plurality of neurons at each of successive time bins;
calculating filter coefficients which when applied to the neural control vector reconstructs motor control parameters.
9. The method of claim 8 further comprising calibration by tracking a stimulus moving through a motor workspace in at least one spatial dimension.
10. The method of claim 9 further comprising calibration based on a psuedorandom tracking task.
11. The method of claim 8 further comprising calibration whereby a user acquires stationary and moving targets in at least one spatial dimension using a neural control signal with previously generated filters and neural and kinematic data concurrent with the target acquisition to build new filters.
12. The method of claim 8 wherein the sensor comprises an array of electrical sensing elements, and the neural control vector represents the sensed electrical activity at each of the electrical sensing elements at each of the successive time bins.