IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Jun 2004
Comparative StudyModeling and decoding motor cortical activity using a switching Kalman filter.
We present a switching Kalman filter model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A "hidden state" models the probability of each mixture component and evolves over time in a Markov chain. The model generalizes previous encoding and decoding methods, addresses the non-Gaussian nature of firing rates, and can cope with crudely sorted neural data common in on-line prosthetic applications.
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IEEE Trans Biomed Eng · Jun 2004
Comparative StudyChronic neural recording using silicon-substrate microelectrode arrays implanted in cerebral cortex.
An important aspect of the development of cortical prostheses is the enhancement of suitable implantable microelectrode arrays for chronic neural recording. The objective of this study was to investigate the recording performance of silicon-substrate micromachined probes in terms of reliability and signal quality. These probes were found to consistently and reliably provide high-quality spike recordings over extended periods of time lasting up to 127 days. ⋯ More than 90% of the probe sites consistently recorded spike activity with signal-to-noise ratios sufficient for amplitudes and waveform-based discrimination. Histological analysis of the tissue surrounding the probes generally indicated the development of a stable interface sufficient for sustained electrical contact. The results of this study demonstrate that these planar silicon probes are suitable for long-term recording in the cerebral cortex and provide an effective platform technology foundation for microscale intracortical neural interfaces for use in humans.
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IEEE Trans Biomed Eng · Jun 2004
Comparative StudyAscertaining 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. ⋯ 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.
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IEEE Trans Biomed Eng · Jun 2004
Comparative StudyEvaluation of spike-detection algorithms for a brain-machine interface application.
Real time spike detection is an important requirement for developing brain machine interfaces (BMIs). We examined three classes of spike-detection algorithms to determine which is best suited for a wireless BMI with a limited transmission bandwidth and computational capabilities. The algorithms were analyzed by tabulating true and false detections when applied to a set of realistic artificial neural signals with known spike times and varying signal to noise ratios. ⋯ Our results indicate that the cost-function scores for the absolute value operator were comparable to those for more elaborate nonlinear energy operator based detectors. The absolute value operator scores were enhanced when the refractory period check was used. Matched-filter-based detectors scored poorly due to their relatively large computational requirements that would be difficult to implement in a real-time system.