Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier

@article{Kim2000NeuralSS,
  title={Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier},
  author={Kyung Hwan Kim and Sung June Kim},
  journal={IEEE Transactions on Biomedical Engineering},
  year={2000},
  volume={47},
  pages={1406-1411}
}
  • Kyung Hwan Kim, S. Kim
  • Published 2000
  • Computer Science, Medicine
  • IEEE Transactions on Biomedical Engineering
Reports a result on neural spike sorting under conditions where the signal-to-noise ratio is very low. The use of nonlinear energy operator enables the detection of an action potential, even when the SNR is so poor that a typical amplitude thresholding method cannot be applied. The superior detection ability facilitates the collection of a training set under lower SNR than that of the methods which employ simple amplitude thresholding. Thus, the statistical characteristics of the input vectors… Expand
Method for unsupervised classification of multiunit neural signal recording under low signal-to-noise ratio
TLDR
A novel unsupervised method that shows satisfactory performance even under high background noise and does not require accurate estimation of the number of units present in the recording and, thus, is better suited for use in fully automated systems. Expand
Automatic spike detection based on adaptive template matching for extracellular neural recordings
TLDR
A new detection algorithm based on template matching that only requires the user to specify the minimum and maximum firing rates of the neurons is described, able to achieve a sensitivity of >90% with a false positive rate of <5Hz in recordings with an estimated SNR=3. Expand
Hardware Efficient Automatic Thresholding for NEO-Based Neural Spike Detection
TLDR
A new approach is presented to automatically set the threshold, in real time, in a manner that is robust to the spike firing rate and suitable for a neural implant. Expand
Detection of neuronal spikes using an adaptive threshold based on the max–min spread sorting method
TLDR
A novel adaptive threshold based on the max-min spread sorting method, which uses the reduced features of raw signal to determine the threshold, thereby giving a simple data manipulation that is beneficial for reducing the computational load when dealing with very large amounts of data. Expand
A neural network for online spike classification that improves decoding accuracy
TLDR
The goals were to automate the intuition of human spike-sorters to operate in real time with an easily tunable parameter governing the stringency with which spike waveforms are classified and to demonstrate that using the network’s classifications for noise removal could improve decoding performance with little risk of harm. Expand
VLSI architecture of NEO spike detection with noise shaping filter and feature extraction using informative samples
  • Linh Hoang, Zhi Yang, Wentai Liu
  • Computer Science, Medicine
  • 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
  • 2009
TLDR
A VLSI architecture of a neural signal processor that can reliably detect spike via a nonlinear energy operator, enhance spike signal over noise ratio by a noise shaping filter, and select meaningful recording samples for clustering by using informative samples is presented. Expand
A New Spike Detection Algorithm for Extracellular Neural Recordings
TLDR
A new algorithm for spike detection has been developed: this applies a cepstrum of bispectrum (CoB) estimated inverse filter to provide blind equalization to find a sequence of event times or delta sequence. Expand
Computationally Efficient Neural Feature Extraction for Spike Sorting in Implantable High-Density Recording Systems
  • A. Kamboh, A. Mason
  • Computer Science, Medicine
  • IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • 2013
TLDR
A new set of spike sorting features, explicitly framed to be computationally efficient and shown to outperform principal component analysis (PCA)-based spike sorting, is described and a hardware friendly architecture, feasible for implantation, is presented. Expand
Neural spike sorting by rough set method
TLDR
A novel method of multineuronal spike sorting based on rough set theory is proposed and the trained rough set classifier yields satisfactory correct ratio on synthesis data. Expand
A wavelet based Teager energy operator for spike detection in microelectrode array recordings
Spike detection in neural recordings is the initial step in the creation of brain machine interfaces. The Teager energy operator (TEO) treats a spike as an increase in the ‘local’ energy and detectsExpand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 30 REFERENCES
A neural network approach to real-time spike discrimination during simultaneous recording from several multi-unit nerve filaments
A multi-channel, real-time, unsupervised spike discriminator was developed in order to reconstruct single spike trains from several simultaneously recorded multi-unit nerve filaments. The programExpand
Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an on-line real-time neural network
TLDR
A connectionist neural network was applied to the spike sorting challenge and performed as well as the MTF in identifying nonoverlapping spikes, and was significantly better in resolving superpositions and rejecting noise. Expand
A review of methods for spike sorting: the detection and classification of neural action potentials.
TLDR
This article reviews algorithms and methods for detecting and classifying action potentials, a problem commonly referred to as spike sorting and discusses the advantages and limitations of each and the applicability of these methods for different types of experimental demands. Expand
Optimal discrimination and classification of neuronal action potential waveforms from multiunit, multichannel recordings using software-based linear filters
Describes advanced protocols for the discrimination and classification of neuronal spike waveforms within multichannel electrophysiological recordings. The programs are capable of detecting andExpand
A new interpretation of nonlinear energy operator and its efficacy in spike detection
TLDR
A new interpretation of NEO is given and it is shown that NEO accentuates the high-frequency content, which makes it an ideal tool for spike detection. Expand
A totally automated system for the detection and classification of neural spikes
  • X. Yang, S. Shamma
  • Computer Science, Medicine
  • IEEE Transactions on Biomedical Engineering
  • 1988
TLDR
A system for neural spike detection and classification is presented which does not require a priori assumptions about spike shape or timing and is illustrated by using it to classify spikes in segments of neural activity recorded from monkey motor cortex and from guinea pig and ferret auditory cortexes. Expand
Reading a Neural Code
TLDR
Here the neural code was characterized from the point of view of the organism, culminating in algorithms for real-time stimulus estimation based on a single example of the spike train, applied to an identified movement-sensitive neuron in the fly visual system. Expand
Automated analyzer for on-line recognition of neural waveforms in extracellular recordings of multiple neurons
TLDR
In most neuroscience experiments, the management of stimuli and recordings becomes more efficient and effective if the response of individual neurons is known to the scientist at the time of the recording rather than at a later time, off-line, therefore on-line operation is highly desirable. Expand
A Comparison of Techniques for Classification of Multiple Neural Signals
A number ofmultiunit neural signal classification techniques are compared in their theoretical separation properties and in their empirical performance in classifying two channel recordings from theExpand
Noise performance design of CMOS preamplifier for the active semiconductor neural probe
TLDR
It was showed that a little deviation of the input device sizes and transconductance ratio from the optimal values can significantly deteriorate the SNR. Expand
...
1
2
3
...