Toward Optimal Target Placement for Neural Prosthetic Devices

Cunningham, John P.; Yu, Byron M.; Gilja, Vikash; Ryu, Stephen I.; Shenoy, Krishna V.

doi:10.1152/jn.90833.2008

Cited by 23 publications

(22 citation statements)

References 54 publications

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“…We trained a rhesus monkey (Macaca mulatta), monkey L, in a standard reaching paradigm that has been extensively reported elsewhere [25], [28], [29]. We give a short overview here.…”

Section: A Animal Task and Neural Recordingsmentioning

confidence: 99%

“…First, we use a simple maximum likelihood (ML) decoder, as seen in [15], [25]. This method uses training data to build an expectation, for reaches to each of the reach targets, of the number of spikes recorded from each neuron.…”

Section: B Neural Prosthetic Decodingmentioning

confidence: 99%

“…Given test data, the ML decoder evaluates the likelihood (under a Poisson noise model) and picks the reach condition with the largest value (hence maximum likelihood) as the decoded reach. The percentage of reach conditions correctly decoded is reported as overall performance [25].…”

Section: B Neural Prosthetic Decodingmentioning

confidence: 99%

“…However, moving to a clinically viable system will require several significant developments. These developments exist at all stages: in the recording technologies [24], the signal processing backend, and prosthetic end effectors such as robotic arms and computer interfaces [25]. This study introduces several problems in the signal processing domain.…”

Section: Introductionmentioning

confidence: 99%

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Neural prosthetic systems: Current problems and future directions

Chestek¹,

Cunningham²,

Gilja³

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-By decoding neural activity into useful behavioral commands, neural prosthetic systems seek to improve the lives of severely disabled human patients. Motor decoding algorithms, which map neural spiking data to control parameters of a device such as a prosthetic arm, have received particular attention in the literature. Here, we highlight several outstanding problems that exist in most current approaches to decode algorithm design. These include two problems that we argue will unlikely result in further dramatic increases in performance, specifically spike sorting and spiking models. We also discuss three issues that have been less examined in the literature, and we argue that addressing these issues may result in dramatic future increases in performance. These include: non-stationarity of recorded waveforms, limitations of a linear mappings between neural activity and movement kinematics, and the low signal to noise ratio of the neural data. We demonstrate these problems with data from 39 experimental sessions with a non-human primate performing reaches and with recent literature. In all, this study suggests that research in cortically-controlled prosthetic systems may require reprioritization to achieve performance that is acceptable for a clinically viable human system.

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“…We trained a rhesus monkey (Macaca mulatta), monkey L, in a standard reaching paradigm that has been extensively reported elsewhere [25], [28], [29]. We give a short overview here.…”

Section: A Animal Task and Neural Recordingsmentioning

confidence: 99%

Section: B Neural Prosthetic Decodingmentioning

confidence: 99%

Section: B Neural Prosthetic Decodingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural prosthetic systems: Current problems and future directions

Chestek¹,

Cunningham²,

Gilja³

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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show abstract

“…The final parameter values were selected as the modal values (within the algorithm convergence tolerance of 10 26 ) from the set of convergent solutions determined by the multiple random starts (Figure 1). This method has been used previously for complex optimisation problems (Boender and Romeijn 1995;Cunningham et al 2008).…”

Section: Computer Methods In Biomechanics and Biomedical Engineering 567mentioning

confidence: 99%

A comparison of cartilage stress-relaxation models in unconfined compression: QLV and stretched exponential in combination with fluid flow

June

Fyhrie

2013

Computer Methods in Biomechanics and Biomedical Engineering

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Cartilage exhibits nonlinear viscoelastic behaviour. Various models have been proposed to explain cartilage stress relaxation, but it is unclear whether explicit modelling of fluid flow in unconfined compression is needed. This study compared Fung's quasi-linear viscoelastic (QLV) model with a stretched-exponential model of cartilage stress relaxation and examined each of these models both alone and in combination with a fluid-flow model in unconfined compression. Cartilage explants were harvested from bovine calf patellofemoral joints and equilibrated in tissue culture for 5 days before stress-relaxation testing in unconfined compression at 5% nominal strain. The stretched exponential models fit as well as the QLV models. Furthermore, the average stretched exponential relaxation time determined by this model lies within the range of experimentally measured relaxation times for extracted proteoglycan aggregates, consistent with the hypothesis that the stretched exponential model represents polymeric mechanisms of cartilage viscoelasticity.

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