Resolution of Spike Overlapping by Biogeography-Based Optimization (original) (raw)
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Current Directions in Biomedical Engineering, 2015
When recording action potentials (spikes) from many neurons simultaneously via multichannel micro-electrodes the overlapping of spikes from different neurons is a demanding problem for detection and classifi-cation of spikes (spike sorting). Since multichannel electrodes provide better possibilities to separate the superimposed waveforms, we refined an algorithm for separation of overlapping spikes for the use on multichannel recordings and tested it on simulated data with different numbers of signal channels and with several signal parameters. We show that the larger the number of signal channels the better the separation that may be achieved, especially under demanding recording conditions.
Spike sorting in the frequency domain with overlap detection
2003
This paper deals with the problem of extracting the activity of individual neurons from multi-electrode recordings. Important aspects of this work are: 1) the sorting is done in two stages -a statistical model of the spikes from different cells is built and only then are occurrences of these spikes in the data detected by scanning through the original data, 2) the spike sorting is done in the frequency domain, 3) strict statistical tests are applied to determine if and how a spike should be classiffed, 4) the statistical model for detecting overlaping spike events is proposed, 5) slow dynamics of spike shapes are tracked during long experiments. Results from the application of these techniques to data collected from the escape response system of the American cockroach, Periplaneta americana, are presented.
A Robust Method for Spike Sorting With Automatic Overlap Decomposition
IEEE Transactions on Biomedical Engineering, 2006
Spike sorting is the mandatory first step in analyzing multiunit recording signals for studying information processing mechanisms within the nervous system. Extracellular recordings usually contain overlapped spikes produced by a number of neurons adjacent to the electrode, together with unknown background noise, which in turn induce some difficulties in neural signal identification. In this paper, we propose a robust method to deal with these problems, which employs an automatic overlap decomposition technique based on the relaxation algorithm that requires simple fast Fourier transforms. The performance of the presented system was tested at various signal-to-noise ratio levels based on synthetic data that were generated from real recordings.
A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021
Spike sorting technologies support neuroscientists to access the neural activity with singleneuron or single-action-potential resolutions. However, conventional spike sorting technologies perform the feature extraction and the clustering separately after the spikes are well detected. It not only induces many redundant processes, but it also yields a lower accuracy and an unstable result especially when noises and/or overlapping spikes exist in the dataset. To address these issues, this paper proposes a unified optimization model integrating the feature extraction and the clustering for spike sorting. Unlike the widely used combination strategies, i.e., performing the principal component analysis (PCA) for spike feature extraction and the K-means (KM) for clustering in sequence, interestingly, this paper finds the solution of the proposed unified model by iteratively performing PCA and KM-like procedures. Subsequently, by embedding the K-means++ strategy in KM-like initializing and a comparison updating rule in the solving process, the proposed model can well handle the noises and overlapping interference as well as enjoy a high accuracy and a low computational complexity. Finally, an automatic spike sorting method is derived after taking the best of the clustering validity indices into the proposed model. The extensive numerical simulation results on both synthetic and real-world datasets confirm that our proposed method outperforms the related state-of-the-art approaches.
A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings
PloS one, 2013
We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a po...
Performance evaluation of PCA-based spike sorting algorithms
Computer Methods and Programs in Biomedicine, 2008
c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 9 1 ( 2 0 0 8 ) 232-244 Noise Single-electrode extracellular recordings a b s t r a c t Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithm's performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white
Spike sorting is one of the most important data analysis problems in neurophysiology. The precision in all steps of the spike-sorting procedure critically affects the accuracy of all subsequent analyses. After data preprocessing and spike detection have been carried out properly, both feature extraction and spike clustering are the most critical subsequent steps of the spike-sorting procedure. The proposed spike sorting approach comprised a new feature extraction method based on shape, phase, and distribution features of each spike (hereinafter SS-SPDF method), which reveal significant information of the neural events under study. In addition, we applied an efficient clustering algorithm based on K-means and template optimization in phase space (hereinafter K-TOPS) that included two integrative clustering measures (validity and error indices) to verify the cohesion-dispersion among spike events during classification and the misclassification of clustering, respectively. The proposed method/ algorithm was tested on both simulated data and real neural recordings. The results obtained for these datasets suggest that our spike sorting approach provides an efficient way for sorting both single-unit spikes and overlapping waveforms. By analyzing raw extracellular recordings collected from the rostral-medial prefrontal cortex (rmPFC) of behaving rabbits during classical eyeblink conditioning, we have demonstrated that the present method/algorithm performs better at classifying spikes and neurons and at assessing their modulating properties than other methods currently used in neurophysiology. Spike-sorting methods have received intensive attention in neurophysiology, and multiple alternative solutions have been proposed during the past few years 1-8. Some studies on spike sorting have been concerned with simplifying the common steps of sorting processes based on mathematical transformations of the raw neural recording to obtain a new signal that would discriminate among spike waveforms originating from different neurons, which presumably correspond to different groups 9-11. With that approach, the common spike-detection and spike-identification steps have been simplified, reducing the computational costs in function of their execution times, but other non-common steps (e.g., raw signal segmentation, local maxima selection, and noisy spike discrimination) were inevitably introduced in the spike-sorting process. In other published works, the focus of attention has been on the feature extraction methods 1-3,12-23. Too often, misapplication of the feature extraction step leads to an extreme reduction of dimensionality and, therefore, the resulting feature matrices correspond with "abstract" mathematical entities (based on coefficients, factors, or components) that do not reflect the main functional properties of the neural events under study. However, some of the works most cited 7,24-32 on spike sorting continue to focus on the effort to develop robust and non-redundant spike-sorting algorithms based on the exhaustive extraction of features with a clear physiological description of the spike event. This physiological information of the spike event is highly appreciated in the qualitative and quantitative characterization of the neuronal activity (intracellular, extracellular, or multi-electrode-array recordings) and has practical uses in neurophysiology beyond the mere spike classification 29,33-38. In particular, extracellular microelectrode recordings can include action potentials from multiple neurons. As the microelectrode tip is surrounded by many neurons, it detects the occurrence of the electrical
A novel automated spike sorting algorithm with adaptable feature extraction
Journal of Neuroscience Methods, 2012
New feature extraction approach based on review of common spike sorting methods. Variable feature derivation by evaluating the feature's probability distribution. Validation of the algorithm with neuronal monolayer and 3D spheroid signal data. Comparison of the results with common feature extraction techniques.
Spike sorting: a novel shift and amplitude invariant technique
Neurocomputing, 2002
This paper deals with the spike classiÿcation problem encountered in multi-unit recordings of neural activity in the brain. We recently developed a new methodology for estimating and classifying multi-units recorded by means of multichannel silicon probes from the observed spike trains (
Spike Superposition Resolution in Multichannel Extracellular Neural Recordings: A Novel Approach
Handbook of Neural Engineering
A classical problem in processing multichannel neural recordings is the ability to resolve temporal superposition of multiple spike waveforms. This situation often occurs when two or more neurons fire simultaneously in the vicinity of the recording microprobe array. In this work, we propose a powerful methodology for resolving this problem with no constraints on the time window in which the superposition occurs or the amount of overlap between spike waveforms. The methodology is part of an ongoing effort to develop a fully automated, optimal system to enhance the signal processing technology of microimplanted devices used for recording and stimulating neural cells at the micro-scale. Simulation results show that the proposed method has a substantial degree of success in resolving an arbitrary number of spikes overlapped together in time without the need for template matching procedures commonly used in offline analysis.