DIRECT CORTICAL CONTROL OF 3D NEUROPROSTHETIC DEVICES PDF

A co-adaptive algorithm uses the firing rate of the electrode sensed neurons or neuron groupings to help develop the control signals. In a closed-loop environment, where the animal subject 10 can view its results 34 , weighting factors in the algorithm are modified over a series of tests to emphasize cortical electrical impulses that result in movement of the object 40 as desired. At the same time, the animal subject 10 learns and modifies its cortical electrical activity to achieve movement of the object 40 as desired. In one specific embodiment, the object moved was a cursor portrayed as a sphere 40 in a virtual reality display 32,

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The method according to claim 66, wherein N[. An electrical controller comprising a computational processor programmed to operate as defined in any one of claims 19 Programming for a computational processor having routines for effecting the method of any one of claims 19 Taylor and Andrew B.

Schwartz, filed Nov. Schwartz, filed Feb. BACKGROUND [] This invention relates to methods and apparatus for control of devices using physiologically-generated electrical impulses, and more particularly to such methods and apparatuses in which neuron electrical activity is sensed by electrodes implanted in or on an animal or a human subject and is translated into control signals adapted by computer program algorithm to control a prosthesis, a computer display, another device or a disabled limb.

For example, individuals incapable of speaking, but capable of the use of a keyboard, have been afforded the opportunity to communicate by computer, computer keyboard and monitor.

Those who have lost the use of their legs have been able to use hand control for either manually driven or motor operated wheelchairs. Tetraplegic individuals have been afforded the opportunity to control, for example, a wheelchair using mouth tubes into which they could blow.

Such techniques are limited in their ability to afford the severely disabled the range of communications and activities of which such individuals are capable mentally. These then could be used for the control of e. Researchers have anticipated using these signals to control various devices directly [1, 2]. One of the difficulties with this approach is that many neurons can be needed to predict intended movement direction accurately enough to make this prediction practical.

Estimates range from 40 to cells or more [4, 7]. Prior studies made their estimates based on open-loop experiments by recreating arm trajectories from cortical data off-line. This prior work does not examine a closed-loop situation in which the subjects have visual feedback of the brain-controlled movement, allowing them to make on-line corrections by modifying their recorded activity.

Chapin, Moxon, Markowitz and Nicolelis [3] used both principle component analysis and recurrent artificial neural networks to decode in real time one-dimensional cortical signals during lever-push movements in a rat. They also used these real-time signals to control a robot. However, these experiments were open-loop with the animal receiving no feedback from the robot.

Meeker et al. Serruya, Hatsopoulos, Paninski, Fellows and Donoghue [30] used linear filters to produce two-dimensional brain-controlled cursor movements in one monkey. Their monkey was able to successfully get a brain-controlled cursor to random targets at a speed close to that achieved with the cursor under hand-control. A high cursor gain allowed the brain-controlled cursor to move fast.

However, there was little precision or endpoint control in the movements. Their best published trajectories overshot and oscillated around the targets before finally hitting them. Holding the cursor stationary in the target was not a requirement of the task. Schnmidt, Bak, McIntosh, and Thomas [32] used operant conditioning to train monkeys to control the firing rates of individual motor cortex cells.

Fetz and Finocchio [12] demonstrated that, with operant conditioning, motor cortex cells can be trained to alter their firing correlations to muscle activity. These closed-loop animal studies suggest a high level of trainability in cortical cells. This plasticity makes cortical cells very desirable as control signals. This technique has enabled them to record from the same cells for an extended period of time.

However, the number of recorded cells is low - about one or two per implant. Even with this small number of signals, this type of implant has allowed locked-in patients to communicate using the firing rates of these cells to scroll through and select letters from a list. This limited use of electrodes in the first human patients has shown that motor cortex cells can be trained to produce useful modulation patterns, even after long periods of inactivity.

More broadly however, the techniques and apparatus of the invention should enable the development of electrical control signals based upon electrical impulses that are available from other regions of the brain, from other regions of the nervous system and from locations where electrical impulses are detected in association with actual or attempted muscle contraction and relaxation.

The methods and apparatus of this invention provide electrical control signals to enable the use of cortical signals to, inter alia, move a computer cursor, steer a wheelchair, control a prosthetic limb or activate muscles in a paralyzed limb. This can provide new levels of mobility and productivity for the severely disabled. Although the inventors used a moving average of the firing rates of the cells, this invention could be used with other characteristics of the subject physiologically-generated electrical signals such as the amplitude of the local field potentials, the power in the different frequencies of the local field potentials, or the amplitude or frequency content of the muscle-associated electrical activity.

In accordance with an algorithm used in the specific exemplary embodiment of the Detailed Description, to calculate the distance to move an object, a normalized firing rate in a time window is calculated. A digital processing device such as a computer or computerized controller applies the firing rate information to determine movement using the programmed algorithm.

The moveable object then is moved a distance depending on at least a portion of the weighted firing rate-related value. Neuron-generated electrical signals are transmitted to the computerized processing device. That device, may be a computer, a computerized prosthetic device or an especially adapted interface capable of digital processing. It may be used to activate nerves that contact the muscles of a disabled limb. Typically, in accordance with the preferred embodiment, the object to be controlled by the subject is moved in the visual field of the subject.

This familiarizes the subject with the task at hand. In each case, for the purpose of reinforcement, the subject may be afforded a reward upon achievement of a predetermined, desired movement of the object. Local field potentials are slower fluctuations in voltage due to the changes in ion concentrations related to post synaptic potentials in the dendrites of many neurons as opposed to the firing rate which is a count of the action potentials in one or a few recorded cells in a given time window.

Any or all of these types of signals can be used in combination. Researchers have shown that local field potentials and muscle activity can be willfully controlled.

Here the invention provides a markedly improved way of translating these signals into multi-dimensional movements. The type of signals to go into the coadaptive algorithm can be quite broad, although firing rates are used as the electrical characteristic of the sensed electrical impulses in the exemplary embodiment of the Detailed Description.

That is, subtracting the means calculated either as a stationary value from previously recorded data or taken over a large moving window and dividing by some value which will standardize the range of values e.

The processor applies the characteristics of the detected electrical impulses to develop a signal with representations of distance and direction. In the visual field of the subject, the object moves a distance and in a direction represented by the calculated signal.

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