Spike Sorting: The First Step in Decoding the Brain: The first step in decoding the brain

Gibson, Sarah; Judy, Jack W.; Marković, Dejan

doi:10.1109/msp.2011.941880

Cited by 163 publications

(145 citation statements)

References 74 publications

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“…There are several methods available (Xu and Wunsch, 2010) for this that include but are not limited to: k-means clustering (Chan et al, 2008), Bayesian clustering (Dai et al, 2008), valley detection (Kim and McNames, 2007;Mahmud et al, 2012), superparamagnetic clustering (Quiroga et al, 2004), expectation maximization (Harris et al, 2000;Pouzat et al, 2002) and artificial neural networks (ANN). k-means is one of the most frequent utilised in offline sorting (Gibson et al, 2012). This method is additionally utilised in other applications where hardware-based clustering has been demonstrated (Winterstein et al, 2013).…”

Section: Clustering Algorithmsmentioning

confidence: 99%

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting

Paraskevopoulou

Eftekhar

et al. 2014

Journal of Neuroscience Methods

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This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation.

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Section: Clustering Algorithmsmentioning

confidence: 99%

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting

Paraskevopoulou

Eftekhar

et al. 2014

Journal of Neuroscience Methods

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“…For [54][55][56]. In the case of extracellularly recorded action potentials (spikes), spike sorting [57] is an efficient process to achieve on-chip data reduction, thereby enabling lower data rate wireless transmission and low power consumption. Neural recording microsystems with data reduction capability typically employ some sort of thresholding to detect and extract the neural information [58,59].…”

Section: Data Reduction Techniques For Multichannel Neuralmentioning

confidence: 99%

Advances in Microelectronics for Implantable Medical Devices

Demosthenous

2014

Advances in Electronics

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Implantable medical devices provide therapy to treat numerous health conditions as well as monitoring and diagnosis. Over the years, the development of these devices has seen remarkable progress thanks to tremendous advances in microelectronics, electrode technology, packaging and signal processing techniques. Many of today's implantable devices use wireless technology to supply power and provide communication. There are many challenges when creating an implantable device. Issues such as reliable and fast bidirectional data communication, efficient power delivery to the implantable circuits, low noise and low power for the recording part of the system, and delivery of safe stimulation to avoid tissue and electrode damage are some of the challenges faced by the microelectronics circuit designer. This paper provides a review of advances in microelectronics over the last decade or so for implantable medical devices and systems. The focus is on neural recording and stimulation circuits suitable for fabrication in modern silicon process technologies and biotelemetry methods for power and data transfer, with particular emphasis on methods employing radio frequency inductive coupling. The paper concludes by highlighting some of the issues that will drive future research in the field.

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“…Extracellular recording of physiological neural activity is the dominant experimental technique in electrophysiology providing valuable information about both the central and peripheral nervous systems [1]. Extracellular electrodes such as hooks or suction electrodes are considered to be both reliable and minimally invasive recording approaches.…”

Section: Introductionmentioning

confidence: 99%

“…This time-consuming process has since been replaced by more reliable computer based, automated methods that make use of statistical analysis and signal processing [3], [4], [5]. There are many different approaches to automated spike sorting but the majority can be broken down into seven main steps [6]:…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Method for Neural Spike Alignment: The Centroid Filter

Metcalfe

Clarke

Donaldson

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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Highlights A key problem in neural spike sorting is highlighted.  A new method to measure the position of action potentials in real-time is described.  This method is validated using detailed simulations. Major improvements are demonstrated compared to standard methods. AbstractBackground: This paper describes a new method to calculate the positions of action potentials (APs) in extracellular neural recordings in real-time. Extracellular recordings made using various types of electrode structures are a key tool in experimental electrophysiology and involve the identification of signals from individual neurons, a process known by the generic name spike sorting. An essential feature of the process is the alignment of individual APs ('spikes') in time. Given the recordings are invariably made in noisy environments there is a tendency to timing jitter leading to alignment errors. This is especially true if single point methods of AP location are employed. New Method: The problems of single point AP location in the presence of noise are significantly reduced by the use of the centroid as a measure of position, which is based on a time average of all the points in the AP. The method is described and analysed in detail and it is shown that a very economical hardware realisation is possible. Results:The new methods are investigated by simulation using deterministic models of nerve signals (i.e. APs with added noise; both correlated and uncorrelated noise models are considered) as well as results measured from a hardware implementation. A power efficient realisation of the centroid filter is demonstrated using a complex programmable logic device (CPLD). Comparison with Existing Method:Detailed simulations demonstrate the superiority of the centroid method compared to several standard, single point metrics. Conclusions: A new method has been suggested that significantly improves one of the key issues in neural spike sorting. The technique has the potential to influence significantly the design of electrophysiological recording systems in the future.

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Spike Sorting: The First Step in Decoding the Brain: The first step in decoding the brain

Cited by 163 publications

References 74 publications

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting

Advances in Microelectronics for Implantable Medical Devices

A New Method for Neural Spike Alignment: The Centroid Filter

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