ABSTRACT
A fully implantable neural data acquisition system is a key component of a clinically viable brain-machine interface. This type of system must communicate with the outside world and obtain power without the use of wires that cross through the skin. We present a 96-channel fully implantable neural data acquisition system. This system performs spike detection and extraction within the body and wirelessly transmits data to an external unit. Power is supplied wirelessly through the use of inductively coupled coils. The system was implanted acutely in sheep and successfully recorded, processed and transmitted neural data. Bidirectional communication between the implanted system and an external unit was successful over a range of 2 m. The system is also shown to integrate well into a brain-machine interface. This demonstration of a high channel-count fully implanted neural data acquisition system is a critical step in the development of a clinically viable brain-machine interface.
Subject(s)
Brain/physiology , Equipment Design , Prostheses and Implants , Signal Processing, Computer-Assisted , Action Potentials , Animals , Computers , Electrodes, Implanted , Haplorhini , Sheep , Telemetry , Temperature , User-Computer InterfaceABSTRACT
In models of electrical stimulation of the nervous system, the electric potential is typically calculated using the quasi-static approximation. The quasi-static approximation allows Maxwell's equations to be simplified by ignoring capacitive, inductive and wave propagation contributions to the potential. While this simplification has been validated for bioelectric sources, its application to rapid stimulation pulses, which contain more high-frequency power, may not be appropriate. We compared the potentials calculated using the quasi-static approximation with those calculated from the exact solution to the inhomogeneous Helmholtz equation. The mean absolute errors between the two potential calculations were limited to 5-13% for pulse widths commonly used for neural stimulation (25 micros-1 ms). We also quantified the excitation properties of extracellular point source stimulation of a myelinated nerve fiber model using potentials calculated from each method. Deviations between the strength-duration curves for potentials calculated using the quasi-static (sigma = 0.105 S m(-1)) and Helmholtz approaches ranged from 3 to 16%, with the minimal error occurring for 100 micros pulses. Differences in the threshold-distance curves for the two calculations ranged from 0 to 9%, for the same value of quasi-static conductivity. A sensitivity analysis of the material parameters revealed that the potential was much more strongly dependent on the conductivity than on the permittivity. These results indicate that for commonly used stimulus pulse parameters, the exact solution for the potential can be approximated by quasi-static simplifications only for appropriate values of conductivity.
Subject(s)
Electric Stimulation/methods , Evoked Potentials/physiology , Nervous System Physiological Phenomena , Algorithms , Electric Conductivity , Humans , Models, Neurological , Models, Statistical , Myelin Sheath/physiology , Nerve Fibers/physiology , Poisson DistributionABSTRACT
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.
