Classification of non-stationary neural signals

Snider, Ross; Bonds, A. B.

doi:10.1016/s0165-0270(98)00110-1

Cited by 55 publications

(41 citation statements)

References 16 publications

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“…There are many parsimonious models of the covariance matrices Σ g of Gaussian distributions; we choose a shared-volume model so that all clusters are approximately the same "size" in feature space. 6 Commonly, nonspike events are included in the data Y k due to mistakes made by upstream components of neural signal analysis (e.g., spike detection and alignment) and must be identified as outliers. To capture these outlier observations, a uniform "background" distribution f 0 is added to the mixture model:…”

Section: A Model Classesmentioning

confidence: 99%

“…During these recordings, the spike waveforms often evolve over time due to electrode drift and other causes, even without active electrode movement [6]. Dividing these long recordings into short time intervals for analysis can improve spike sorting results, as the data are apt to be effectively stationary over these brief intervals [7], [8].…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian Clustering Method for Tracking Neural Signals Over Successive Intervals

Wolf

Burdick

2009

IEEE Trans. Biomed. Eng.

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Abstract-This paper introduces a new, unsupervised method for sorting and tracking the action potentials of individual neurons in multiunit extracellular recordings. Presuming the data are divided into short, sequential recording intervals, the core of our strategy relies upon an extension of a traditional mixture model approach that incorporates clustering results from the preceding interval in a Bayesian manner, while still allowing for signal nonstationarity and changing numbers of recorded neurons. As a natural byproduct of the sorting method, current and prior signal clusters can be matched over time in order to track persisting neurons. We also develop techniques to use prior data to appropriately seed the clustering algorithm and select the model class. We present results in a principal components space; however, the algorithm may be applied in any feature space where the distribution of a neuron's spikes may be modeled as Gaussian. Applications of this signal classification method to recordings from macaque parietal cortex show that it provides significantly more consistent clustering and tracking results than traditional methods based on expectation-maximization optimization of mixture models. This consistent tracking ability is crucial for intended applications of the method.

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Section: A Model Classesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bayesian Clustering Method for Tracking Neural Signals Over Successive Intervals

Wolf

Burdick

2009

IEEE Trans. Biomed. Eng.

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“…The non-stationarity of spike waveforms due to electrode drift is a commonly cited culprit for difficulties in tracking neurons over time [17], [18], [19]. However, when the recording application involves repeated sampling and clustering over time, our experience has shown that the inconsistency of conventional clustering methods' output is a crucial issue.…”

Section: Bayesian Spike Clustering and Neuron Trackingmentioning

confidence: 99%

A miniature robot for isolating and tracking neurons in extracellular cortical recordings

Wolf

Cham

Branchaud

et al. 2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-This paper presents a miniature robot device and control algorithm that can autonomously position electrodes in cortical tissue for isolation and tracking of extracellular signals of individual neurons. Autonomous electrode positioning can significantly enhance the efficiency and quality of acute electrophysiolgical experiments aimed at basic understanding of the nervous system. Future miniaturized systems of this sort could also overcome some of the inherent difficulties in estabilishing long-lasting neural interfaces that are needed for practical realization of neural prostheses. The paper describes the robot's design and summarizes the overall structure of the control system that governs the electrode positioning process. We present a new sequential clustering algorithm that is key to improving our system's performance, and which may have other applications in robotics. Experimental results in macaque cortex demonstrate the validity of our approach.

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“…Las señales MER están compuestas por la suma de descargas de la población neuronal de un pequeño volumen próximo a la punta del electrodo y presentan un comportamiento no estacionario debido a la contribución de varios factores, como la propia variación de las descargas, que no son exactamente iguales ni exactamente regulares en su ritmo. A esto se añaden otros como la pulsación cortical causada por la actividad cardiaca o respiratoria, la reducción sistemática de la amplitud de un potencial de acción cuando la célula se dispara con alta frecuencia, el movimiento sistemático del electrodo desde el sitio original de registro (Snider & Bonds, 1998) y el ruido neuronal de fondo (Pouzat, Delescluse, Viot, & Diebolt, 2004). La importancia del procesamiento y clasificación de señales MER durante la DBS en pacientes con EP radica en la necesidad de un soporte de decisión para localizar correctamente una zona cerebral objetivo y la respectiva implantación del microelectrodo estimulador.…”

Section: Introductionunclassified

Representación óptima de señales MER aplicada a la identificación de estructuras cerebrales durante la estimulación cerebral profunda

Cardona

Álvarez

Gutiérrez

2015

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InvestIgacIón ResumenLa identificación de señales cerebrales provenientes de microelectrodos de registro (MER) es un procedimiento clave en la estimulación cerebral profunda (DBS en inglés) aplicada en pacientes con enfermedad de Parkinson (EP). El propósito de esta investigación es identificar con alta precisión una estructura cerebral llamada núcleo subtalámico (STN), ya que es la estructura objetivo donde se logran los mejores resultados terapéuticos de la DBS. Para ello, se presenta un enfoque de representación óptima de señales MER mediante el método de Frames (MOF por sus siglas en inglés), con el cual se obtienen coeficientes que minimizan la norma Euclidiana de orden 2. A partir de los coeficientes óptimos se realiza una extracción de características de las señales combinando diccionarios wavelet packet y coseno. Para tener un marco de comparación con el estado del arte, también se caracterizan las señales utilizando la transformada wavelet discreta (DWT) con diferentes funciones madre. La metodología propuesta se valida en una base de datos real, y se emplean máquinas de aprendizaje supervisadas simples, como el clasificador K-Nearest Neighbors (K-NN), el clasificador lineal bayesiano (LDC) y el cuadrático (QDC). Los resultados de clasificación que se obtienen con el método propuesto mejoran significativamente el rendimiento alcanzado con la DWT, de manera que se logra una identificación positiva del STN superior al 97,6%. Los índices de identificación logrados por el MOF son muy precisos, ya que potencialmente se puede obtener una tasa de falsos positivos menores al 2% durante la DBS. Palabras clave: aprendizaje de máquina, enfermedad de Parkinson, estimulación cerebral profunda, procesamiento digital de señales, señales MER. AbstractIdentification of brain signals from microelectrode recordings (MER) is a key procedure during deep brain stimulation (DBS) applied in Parkinson's disease patients. The main purpose of this research work is to identify with high accuracy a brain structure called subthalamic nucleus (STN), since it is the target structure where the DBS achieves the best therapeutic results. To do this, we present an approach for optimal representation of MER signals Representación óptima de señales MER aplicada a la identificación de estructuras cerebrales durante la estimulación cerebral profunda

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Classification of non-stationary neural signals

Cited by 55 publications

References 16 publications

A Bayesian Clustering Method for Tracking Neural Signals Over Successive Intervals

A Bayesian Clustering Method for Tracking Neural Signals Over Successive Intervals

A miniature robot for isolating and tracking neurons in extracellular cortical recordings

Representación óptima de señales MER aplicada a la identificación de estructuras cerebrales durante la estimulación cerebral profunda

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