Biomedical subjects

Jose M Carmena

Publications and source records attributed to Jose M Carmena.

9 recordsLinked to original sources

PubMed2006

Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces.

Research on brain-machine interfaces (BMI's) is directed toward enabling paralyzed individuals to manipulate their environment through slave robots. Even for able-bodied individuals, using a robot to reach and grasp objects in unstructured environments can be a difficult telemanipulation task. Controlling the slave directly with neural signals instead of a hand-master adds further challenges, such as uncertainty about the intended trajectory coupled with a low update rate for the command signal. To address these challenges, a continuous shared control (CSC) paradigm is introduced for BMI where robot sensors produce reflex-like reactions to augment brain-controlled trajectories. To test the merits of this approach, CSC was implemented on a 3-degree-of-freedom robot with a gripper bearing three co-located range sensors. The robot was commanded to follow eighty-three reach-and-grasp trajectories estimated previously from the outputs of a population of neurons recorded from the brain of a monkey. Five different levels of sensor-based reflexes were tested. Weighting brain commands 70% and sensor commands 30% produced the best task performance, better than brain signals alone by more than seven-fold. Such a marked performance improvement in this test case suggests that some level of machine autonomy will be an important component of successful BMI systems in general.

Arm↗

PubMed2005

Stable ensemble performance with single-neuron variability during reaching movements in primates.

Significant variability in firing properties of individual neurons was observed while two monkeys, chronically implanted with multielectrode arrays in frontal and parietal cortical areas, performed a continuous arm movement task. Although the degree of correlation between the firing of single neurons and movement parameters was nonstationary, stable predictions of arm movements could be obtained from the activity of neuronal ensembles. This result adds support to the idea that movement parameters are redundantly encoded in the motor cortex, such that brain networks can achieve the same behavioral goals through different patterns and relative contribution of individual neuron activity. This has important implications for neural prosthetics, suggesting that accurate operation of a brain-machine interface requires recording from large neuronal ensembles to minimize the effect of variability and ensuring stable performance over long periods of time.

Action Potentials↗

PubMed2005

Orbitofrontal ensemble activity monitors licking and distinguishes among natural rewards.

The classification of rhythmic licking into clusters has proved to be useful for characterizing brain mechanisms that modulate the ingestion of natural rewards (sucrose and water). One cortical area that is responsive to rewarding stimuli is the orbitofrontal cortex (OFC). However, it is not presently known how OFC neurons respond while rodents freely lick for natural rewards and whether these responses are related to the structure of licking clusters. We addressed these issues by showing that temporary inactivation of the OFC decreases the duration and increases the number of clusters and that the activity of OFC neurons changed at precise times before, during, and after the cluster terminates. Furthermore, analysis of the activity of OFC neuronal ensembles showed that they could discriminate cluster onset from termination, predict when a behaving animal will begin a cluster, and distinguish and anticipate between natural rewards. These results provide a new role for the OFC in influencing licking clusters and anticipating specific rewards.

Animals↗

PubMed2005

Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface.

Monkeys can learn to directly control the movements of an artificial actuator by using a brain-machine interface (BMI) driven by the activity of a sample of cortical neurons. Eventually, they can do so without moving their limbs. Neuronal adaptations underlying the transition from control of the limb to control of the actuator are poorly understood. Here, we show that rapid modifications in neuronal representation of velocity of the hand and actuator occur in multiple cortical areas during the operation of a BMI. Initially, monkeys controlled the actuator by moving a hand-held pole. During this period, the BMI was trained to predict the actuator velocity. As the monkeys started using their cortical activity to control the actuator, the activity of individual neurons and neuronal populations became less representative of the animal's hand movements while representing the movements of the actuator. As a result of this adaptation, the animals could eventually stop moving their hands yet continue to control the actuator. These results show that, during BMI control, cortical ensembles represent behaviorally significant motor parameters, even if these are not associated with movements of the animal's own limb.

Adaptation, Physiological↗

PubMed2004

Ascertaining the importance of neurons to develop better brain-machine interfaces.

In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble.

Action Potentials↗

PubMed2004

Transmission latencies in a telemetry-linked brain-machine interface.

To be clinically viable, a brain-machine interface (BMI) requires transcutaneous telemetry. Spike-based compression algorithms can be used to reduce the amount of telemetered data, but this type of system is subject to queuing-based transmission delays. This paper examines the relationships between the ratio of output to average input bandwidth of an implanted device and transmission latency and required queue depth. The examination was performed with a computer model designed to simulate the telemetry link. The input to the model was presorted spike data taken from a macaque monkey performing a motor task. The model shows that when the output bandwidth/average input bandwidth is in unity, significant transmission latencies occur. For a 32-neuron system, transmitting 50 bytes of data per spike and with an average neuron firing rate of 8.93 spikes/s, the average maximum delay was approximately 3.2 s. It is not until the output bandwidth is four times the average input bandwidth that average maximum delays are reduced to less than 10 ms. A comparison of neuron firing rate and resulting latencies shows that high latencies result from neuron bursting. These results will impact the design of transcutaneous telemetry in a BMI.

Action Potentials↗

PubMed2004

Ensemble recordings of human subcortical neurons as a source of motor control signals for a brain-machine interface.

OBJECTIVE: Patients with severe neurological injury, such as quadriplegics, might benefit greatly from a brain-machine interface that uses neuronal activity from motor centers to control a neuroprosthetic device. Here, we report an implementation of this strategy in the human intraoperative setting to assess the feasibility of using neurons in subcortical motor areas to drive a human brain-machine interface. METHODS: Acute ensemble recordings from subthalamic nucleus and thalamic motor areas (ventralis oralis posterior [VOP]/ventralis intermediate nucleus [VIM]) were obtained in 11 awake patients during deep brain stimulator surgery by use of a 32-microwire array. During extracellular neuronal recordings, patients simultaneously performed a visual feedback hand-gripping force task. Offline analysis was then used to explore the relationship between neuronal modulation and gripping force. RESULTS: Individual neurons (n = 28 VOP/VIM, n = 119 subthalamic nucleus) demonstrated a variety of modulation responses both before and after onset of changes in gripping force of the contralateral hand. Overall, 61% of subthalamic nucleus neurons and 81% of VOP/VIM neurons modulated with gripping force. Remarkably, ensembles of 3 to 55 simultaneously recorded neurons were sufficiently information-rich to predict gripping force during 30-second test periods with considerable accuracy (up to R = 0.82, R(2) = 0.68) after short training periods. Longer training periods and larger neuronal ensembles were associated with improved predictive accuracy. CONCLUSION: This initial feasibility study bridges the gap between the nonhuman primate laboratory and the human intraoperative setting to suggest that neuronal ensembles from human subcortical motor regions may be able to provide informative control signals to a future brain-machine interface.

Electrodes, Implanted↗

PubMed2003

Learning to control a brain-machine interface for reaching and grasping by primates.

Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain-machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.

Animals↗

PubMed2003

Chronic, multisite, multielectrode recordings in macaque monkeys.

A paradigm is described for recording the activity of single cortical neurons from awake, behaving macaque monkeys. Its unique features include high-density microwire arrays and multichannel instrumentation. Three adult rhesus monkeys received microwire array implants, totaling 96-704 microwires per subject, in up to five cortical areas, sometimes bilaterally. Recordings 3-4 weeks after implantation yielded 421 single neurons with a mean peak-to-peak voltage of 115 +/- 3 microV and a signal-to-noise ratio of better than 5:1. As many as 247 cortical neurons were recorded in one session, and at least 58 neurons were isolated from one subject 18 months after implantation. This method should benefit neurophysiological investigation of learning, perception, and sensorimotor integration in primates and the development of neuroprosthetic devices.

Action Potentials↗