Spike Sorting Methods Research Articles

Grasp Movement Decoding from Premotor and Parietal Cortex

Despite recent advances in harnessing cortical motor-related activity to control computer cursors and robotic devices, the ability to decode and execute different grasping patterns remains a major obstacle. Here we demonstrate a simple Bayesian decoder for real-time classification of grip type and wrist orientation in macaque monkeys that uses higher-order planning signals from anterior intraparietal cortex (AIP) and ventral premotor cortex (area F5). Real-time decoding was based on multiunit signals, which had similar tuning properties to cells in previous single-unit recording studies. Maximum decoding accuracy for two grasp types (power and precision grip) and five wrist orientations was 63% (chance level, 10%). Analysis of decoder performance showed that grip type decoding was highly accurate (90.6%), with most errors occurring during orientation classification. In a subsequent off-line analysis, we found small but significant performance improvements (mean, 6.25 percentage points) when using an optimized spike-sorting method (superparamagnetic clustering). Furthermore, we observed significant differences in the contributions of F5 and AIP for grasp decoding, with F5 being better suited for classification of the grip type and AIP contributing more toward decoding of object orientation. However, optimum decoding performance was maximal when using neural activity simultaneously from both areas. Overall, these results highlight quantitative differences in the functional representation of grasp movements in AIP and F5 and represent a first step toward using these signals for developing functional neural interfaces for hand grasping.

Fuzzy logic-based spike sorting system

We present a new method for autonomous real-time spike sorting using a fuzzy logic inference engine. The engine assigns each detected event a ‘spikiness index’ from zero to one that quantifies the extent to which the detected event is like an ideal spike. Spikes can then be sorted by simply clustering the spikiness indices. The sorter is defined in terms of natural language rules that, once defined, are static and thus require no user intervention or calibration. The sorter was tested using extracellular recordings from three animals: a macaque, an owl monkey and a rat. Simulation results show that the fuzzy sorter performed equal to or better than the benchmark principal component analysis (PCA) based sorter. Importantly, there was no degradation in fuzzy sorter performance when the spikes were not temporally aligned prior to sorting. In contrast, PCA sorter performance dropped by 27% when sorting unaligned spikes. Since the fuzzy sorter is computationally trivial and requires no spike alignment, it is suitable for scaling into large numbers of parallel channels where computational overhead and the need for operator intervention would preclude other spike sorters.

Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering

This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximization) were incorporated within the spike sorting algorithms, in order to find a suitable classifier for the feature sets. Simulated data sets and in-vivo tetrode multichannel recordings were employed to assess the performance of the spike sorting algorithms. The results show that the proposed algorithm yields significantly improved performance with mean sorting accuracy of 73% and sorting error of 10% compared to PCA which combined with k-means had a sorting accuracy of 58% and sorting error of 10%.A correction was made to this article on 22 February 2011. The spacing of the title was amended on the abstract page. No changes were made to the article PDF and the print version was unaffected.

Technology-Aware Algorithm Design for Neural Spike Detection, Feature Extraction, and Dimensionality Reduction

Applications such as brain-machine interfaces require hardware spike sorting in order to 1) obtain single-unit activity and 2) perform data reduction for wireless data transmission. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection, feature-extraction, and dimensionality-reduction algorithms for spike sorting are described and evaluated in terms of accuracy versus complexity. The nonlinear energy operator is chosen as the optimal spike-detection algorithm, being most robust over noise and relatively simple. Discrete derivatives is chosen as the optimal feature-extraction method, maintaining high accuracy across signal-to-noise ratios with a complexity orders of magnitude less than that of traditional methods such as principal-component analysis. We introduce the maximum-difference algorithm, which is shown to be the best dimensionality-reduction method for hardware spike sorting.

Performance evaluation of ICA-based spike sorting methods to multi-unit data recorded by a multi-site electrode in the rat hippocampus in vivo

Performance evaluation of ICA-based spike sorting methods to multi-unit data recorded by a multi-site electrode in the rat hippocampus in vivo

A practical review for spike sorting methods

A practical review for spike sorting methods

Neural spike sorting using mathematical morphology, multiwavelets transform and hierarchical clustering

Neural spike sorting is an indispensable first step for the analysis of multiple spike train data. As it is very common that noises degrade the performance of most of the available spike sorting method, in this paper we have proposed a novel spike sorting algorithm framework which seems less susceptible to heavy noises. At first, mathematical morphology operation is used to facilitate the spike event detection process, especially in strong noisy situations. Then, multiwavelets transform is performed to the detected spike waveforms to extract discriminative features. Finally, hierarchical clustering with an outlier removal process proceeds to separate the first 10 distinguishable multiwavelets coefficients. The results show that our spike sorting method performs quite well even for the heavy noisy simulated spike data.

Quantitative estimation of the nonstationary behavior of neural spontaneous activity.

The “stationarity time” (ST) of neuronal spontaneous activity signals of rat embryonic cortical cells, measured by means of a planar Multielectrode Array (MEA), was estimated based on the “Detrended Fluctuation Analysis” (DFA). The ST is defined as the mean time interval during which the signal under analysis keeps its statistical characteristics constant. An upgrade on the DFA method is proposed, leading to a more accurate procedure. Strong statistical correlation between the ST, estimated from the Absolute Amplitude of Neural Spontaneous Activity (AANSA) signals and the Mean Interburst Interval (MIB), calculated by classical spike sorting methods applied to the interspike interval time series, was obtained. In consequence, the MIB may be estimated by means of the ST, which further includes relevant biological information arising from basal activity. The results point out that the average ST of MEA signals lies between 2-3 seconds. Furthermore, it was shown that a neural culture presents signals that lead to different statistical behaviors, depending on the relative geometric position of each electrode and the cells. Such behaviors may disclose physiological phenomena, which are possibly associated with different adaptation/facilitation mechanisms.

Unsupervised wavelet-based spike sorting with dynamic codebook searching and replenishment

Spike information is beneficial for correlating neuronal activity to various stimuli, finding target neural areas for deep brain stimulation, and decoding intended motor command for brain–machine interface. Unsupervised classification based on spike features provides a way to separate spikes generated from different neurons. Here, we propose an unsupervised spike sorting method based on specific wavelet coefficients (SWC) and using both a new spike alignment technique based on multi-peak energy comparison (MPEC) and a dynamic codebook-based template-matching algorithm with a class-merging feature. The MPEC alignment reduced inconsistent alignment caused by spike deformation. Using SWC not only reduced the number of features but also performed better in terms of matching a neuronal spike to its own class than relying on spike waveform or whole wavelet coefficients. Moreover, the employed codebook searching and replenishment can be operated in an online, real-time mode.

Automatic spike sorting using tuning information.

Current spike sorting methods focus on clustering neurons' characteristic spike waveforms. The resulting spike-sorted data are typically used to estimate how covariates of interest modulate the firing rates of neurons. However, when these covariates do modulate the firing rates, they provide information about spikes' identities, which thus far have been ignored for the purpose of spike sorting. This letter describes a novel approach to spike sorting, which incorporates both waveform information and tuning information obtained from the modulation of firing rates. Because it efficiently uses all the available information, this spike sorter yields lower spike misclassification rates than traditional automatic spike sorters. This theoretical result is verified empirically on several examples. The proposed method does not require additional assumptions; only its implementation is different. It essentially consists of performing spike sorting and tuning estimation simultaneously rather than sequentially, as is currently done. We used an expectation-maximization maximum likelihood algorithm to implement the new spike sorter. We present the general form of this algorithm and provide a detailed implementable version under the assumptions that neurons are independent and spike according to Poisson processes. Finally, we uncover a systematic flaw of spike sorting based on waveform information only.

Neural signal classification using a simplified feature set with nonparametric clustering

This paper presents a spike sorting method using a simplified feature set with a nonparametric clustering algorithm. The proposed feature extraction algorithm is efficient and has been implemented with a custom integrated circuit chip interfaced with the PC. The proposed clustering algorithm performs nonparametric clustering. It defines an energy function to characterize the compactness of the data and proves that the clustering procedure converges. Through iterations, the data points collapse into well formed clusters and the associated energy approaches zero. By claiming these isolated clusters, neural spikes are classified.

Comparison of valley seeking and T-distributed EM algorithm for spike sorting

Most analysis methods for neural spike data require knowledge about the exact spike times of individual neurons. To obtain this information from extracellular recordings, spike sorting is necessary to exclude measurement artifacts and to separate spikes from different neurons. The quality of spike sorting can have significant effects on the results of spike train analyses [1]. However, manual spike sorting leads to extremely variable results due to different sorting strategies of individual persons [2]. Therefore, reproducible results require automated spike-sorting methods, even though they also lead to sorting errors.

An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes.

Traditional waveform based spike sorting yields biased rate code estimates

Much of neuroscience has to do with relating neural activity and behavior or environment. One common measure of this relationship is the firing rates of neurons as functions of behavioral or environmental parameters, often called tuning functions and receptive fields. Firing rates are estimated from the spike trains of neurons recorded by electrodes implanted in the brain. Individual neurons' spike trains are not typically readily available, because the signal collected at an electrode is often a mixture of activities from different neurons and noise. Extracting individual neurons' spike trains from voltage signals, which is known as spike sorting, is one of the most important data analysis problems in neuroscience, because it has to be undertaken prior to any analysis of neurophysiological data in which more than one neuron is believed to be recorded on a single electrode. All current spike-sorting methods consist of clustering the characteristic spike waveforms of neurons. The sequence of first spike sorting based on waveforms, then estimating tuning functions, has long been the accepted way to proceed. Here, we argue that the covariates that modulate tuning functions also contain information about spike identities, and that if tuning information is ignored for spike sorting, the resulting tuning function estimates are biased and inconsistent, unless spikes can be classified with perfect accuracy. This means, for example, that the commonly used peristimulus time histogram is a biased estimate of the firing rate of a neuron that is not perfectly isolated. We further argue that the correct conceptual way to view the problem out is to note that spike sorting provides information about rate estimation and vice versa, so that the two relationships should be considered simultaneously rather than sequentially. Indeed we show that when spike sorting and tuning-curve estimation are performed in parallel, unbiased estimates of tuning curves can be recovered even from imperfectly sorted neurons.

A Robust Method for Spike Sorting With Automatic Overlap Decomposition

A Robust Method for Spike Sorting With Automatic Overlap Decomposition

OpenElectrophy: an electrophysiological data and analysis sharing framework

Progress in experimental tools and design is allowing the acquisition of increasingly large datasets. Storage, manipulation and efficient analyses of such large amounts of data is now a primary issue. We present OpenElectrophy, an electrophysiological data- and analysis-sharing framework developed to fill this niche. It stores all experiment data and meta-data in a single central MySQL database, and provides a graphic user interface to visualize and explore the data, and a library of functions for user analysis scripting in Python. It implements multiple spike-sorting methods, and oscillation detection based on the ridge extraction methods due to Roux et al. (2007). OpenElectrophy is open source and is freely available for download at http://neuralensemble.org/trac/OpenElectrophy.

Neuronal Activity of the Human Subthalamic Nucleus in the Parkinsonian and Nonparkinsonian State

We recorded resting-state neuronal activity from the human subthalamic nucleus (STN) during functional stereotactic surgeries. By inserting up to five parallel microelectrodes for single- or multiunit recordings and applying statistical spike-sorting methods, we were able to isolate a total of 351 single units in 65 patients with Parkinson's disease (PD) and 33 single units in 9 patients suffering from essential tremor (ET). Among these were 93 pairs of simultaneously recorded neurons in PD and 17 in ET, which were detected either by the same (n = 30) or neighboring microelectrodes (n = 80). Essential tremor is a movement disorder without any known basal ganglia pathology and with normal dopaminergic brain function. By comparing the neuronal activity of the STN in patients suffering from PD and ET we intended to characterize, for the first time, changes of basal ganglia activity in the human disease state that had previously been described in animal models of Parkinson's disease. We found a significant increase in the mean firing rate of STN neurons in PD and a relatively larger fraction of neurons exhibiting burstlike activity compared with ET. The overall proportion of neurons exhibiting intrinsic oscillations or interneuronal synchronization as defined by significant spectral peaks in the auto- or cross-correlations functions did not differ between PD and ET when considering the entire frequency range of 1-100 Hz. The distribution of significant oscillations across the theta (1-8 Hz), alpha (8-12 Hz), beta (12-35 Hz), and gamma band (>35 Hz), however, was uneven in ET and PD, as indicated by a trend in Fisher's exact test (P = 0.05). Oscillations and pairwise synchronizations within the 12- to 35-Hz band were a unique feature of PD. Our results confirm the predictions of the rate model of Parkinson's disease. In addition, they emphasize abnormalities in the patterning and dynamics of neuronal discharges in the parkinsonian STN, which support current concepts of abnormal motor loop oscillations in Parkinson's disease.

Spike sorting based on multi-class support vector machine with superposition resolution

A new spike sorting method based on the support vector machine (SVM) is proposed to resolve the superposition problem. The spike superposition is generally resolved by the template matching. Previous template matching methods separate the spikes through linear classifiers. The classification performance is severely influenced by the background noise included in spike trains. The nonlinear classifiers with high generation ability are required to deal with the task. A multi-class SVM classifier is therefore applied to separate the spikes, which contains several binary SVM classifiers. Every binary SVM classifier corresponding to one spike class is used to identify the single and superposition spikes. The superposition spikes are decomposed through template extraction. The experimental results on the simulated and real data demonstrate the utility of the proposed method.

A simple method for efficient spike detection in multiunit recordings

A number of spike detection and sorting methods exist and the availability of powerful desktop computers may suggest that the problem of spike detection is solved. However, for portable multi-channel systems, when one takes into account the power consumption limitations, computationally simple methods can be advantageous when compared to more complex algorithms. Here we describe a simple spike detection method that reduces approximately two-fold the rate of false detections compared to the single threshold spike detection method. The proposed algorithm can be employed in an analog electronic chip thus eliminating the need for the digitization of the original signal. Consequently, only the times of spike occurrence can be transmitted for further analysis.

A spike sorting method with optimal feature extraction and clustering

A spike sorting method with optimal feature extraction and clustering