Spike Feature Extraction Research Articles

A comprehensive review of spike sorting algorithms in neuroscience

Spike sorting plays a pivotal role in neuroscience, serving as a crucial step of separating electrical signals recorded from multiple neurons to further analyze neuronal interactions. This process involves separating electrical signals that originate from multiple neurons, recorded through devices like electrode arrays. This is a very important link in the field of brain-computer interfaces. The objective of spike sorting algorithm (SSA) is to distinguish the behavior of one or more neurons from background noise using the waveforms captured by brain-embedded electrodes. This article starts from the steps of the conventional SSA and divides the SSA into three steps: spike detection, spike feature extraction, and spike clustering. It outlines prevalent algorithms for each phase before delving into two emerging technologies: template matching and deep learning-based methods. The discussion on deep learning is further subdivided into three approaches: end-to-end solution, deep learning for spike sorting steps, and spiking neural networks-based solutions. Finally, it elaborates future challenges and development trends of SSAs.

A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting.

Spike sorting technologies support neuroscientists to access the neural activity with single-neuron 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 Novel Spike Detection Algorithm Based on Multi-Channel of BECT EEG Signals

Benign childhood epilepsy with centro-temporal spikes (BECT) is one of the most common epilepsy syndromes in childhood which is typically characterized by localized discharges in the central and temporal regions. Traditionally, the recognition of spikes requires visual assessment of long-term EEG recordings which is time consuming and subjective because it depends on the knowledge and experience of the doctor. Therefore, a novel multi-step spike detection algorithm based on average reference (AV) channel and bipolar (BP) channel BECT EEG is proposed, including candidate spike detection algorithm, false positive spike (FPS) elimination, spike feature extraction and random forest (RF) classification. The proposed method is evaluated using 7 routine EEG recordings. This brief shows that the sensitivity (Sen), specificity (Spe), selectivity (Sel) and accuracy (AC) obtained by the proposed method are 97.4%, 96.5%, 96.6% and 96.9%, respectively. Experimental results show that the proposed method is capable of detecting BECT spikes efficiently.

Computationally efficient fully-automatic online neural spike detection and sorting in presence of multi-unit activity for implantable circuits

BackgroundSpike sorting is a basic step for implantable neural interfaces. With the growing number of channels, the process should be computationally efficient, automatic,robust and applicable on implantable circuits. New MethodThe proposed method is a combination of fully-automatic offline and online processes. It introduces a novel method for automatically determining a data-aware spike detection threshold, computationally efficient spike feature extraction, automatic optimal cluster number evaluation and verification coupled with Self-Organizing Maps to accurately determine cluster centroids. The system has the ability of unsupervised online operation after initial fully-automatic offline training. The prime focus of this paper is to fully-automate the complete spike detection and sorting pipeline, while keeping the accuracy high. ResultsThe proposed system is simulated on two well-known datasets. The automatic threshold improves detection accuracies significantly( > 15%) as compared to the most common detector. The system is able to effectively handle background multi-unit activity with improved performance. ComparisonMost of the existing methods are not fully-automatic; they require supervision and expert intervention at various stages of the pipeline. Secondly, existing works focus on foreground neural activity. Recent research has highlighted importance of background multi-unit activity, and this work is amongst the first efforts that proposes and verifies an automatic methodology to effectively handle them as well. ConclusionThis paper proposes a fully-automatic, computationally efficient system for spike sorting for both single-unit and multi-unit spikes. Although the scope of this work is design and verification through computer simulations, the system has been designed to be easily transferable into an integrated hardware form.

WMsorting: Wavelet Packets Decomposition and Mutual Information Based Spike Sorting Method.

In recent years, the signal processing opportunities with the multi-channel recording and the high precision detection provided by the development of new extracellular multielectrodes are increasing. Hence, designing new spike sorting algorithms are both attractive and challenging. These algorithms are used to distinguish the individual neurons' activity from the dense and simultaneously recorded neural action potentials with high accuracy. However, since the overlapping phenomenon often inevitably arises in the recorded data, they are not accurate enough in practical situations especially when the noise level is high. In this paper, a spike feature extraction method based on the Wavelet Packets Decomposition and the Mutual Information is proposed. This is a highly accurate semi-supervised solution with a short training phase for performing the automation of the spike sorting framework. Further, the evaluations are performed on different public datasets. The raw data is not only suffered from multiple noises (from 5% level to 20% level) but also includes various degrees of overlapping spikes at different times. The clustering results demonstrate the effectiveness of our proposed algorithm. Also, it achieves a good anti-noise performance with ensuring a high clustering accuracy (up to 99.76%) compared with the state of art methods.

Neural Spike Feature Extraction of Globus Pallidus Based on Wavelet Package Decomposition

In this study, features of neural signal recordings with microelectrode injected into the internal globus pallidus of non-human primates before and after 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) treatment which induces Parkinson’s disease (PD) were investigated. Neural oscillations were analyzed using the measure of wavelet spectrum and singular value extraction. Differences in the time-frequency analysis were observed between groups. Results showed that the PD states had significantly higher amplitudes of spectrum. Singular values extracted from wavelet coefficients after wavelet package decomposition were used to differentiate pathological changes from normal states, and the singular values of the reconstructed wavelet coefficients of the PD states were statistically smaller than controls. We speculate that the irregular high frequency oscillations and reduced singular values observed in the pathological firings are consistent with the previous view that a loss of information transmission in the neural circuitry underlies PD and these measurements could be of diagnostic and monitoring values of the disease state.

1/f Neural Noise Reduction and Spike Feature Extraction Using a Subset of Informative Samples

This article describes a study on neural noise and neural signal feature extraction, targeting real-time spike sorting with miniaturized microchip implementation. Neuronal signature, noise shaping, and adaptive bandpass filtering are reported as the techniques to enhance the signal-to-noise ratio (SNR). A subset of informative samples of the waveforms is extracted as features for classification. Quantitative and comparative experiments with both synthesized and animal data are included to evaluate different feature extraction approaches. In addition, a preliminary hardware implementation has been realized using an integrated circuit.

Improving spike separation using waveform derivatives

This paper presents spike derivatives as a tool for spike feature extraction to improve the separation of similar neurons. The theoretical framework of neuronal geometry signatures and noise shaping to perform the spike derivative is formulated first, and based on the derivations we show that the first derivative of the spikes manifests the waveform difference contributed by the geometry signatures and also reduces the associated low-frequency noise. Quantitative comparisons of sorting neurons using spikes and their derivatives are performed on spike sequences from a public database, and improved results are observed when using spike derivatives.

A 128-Channel 6 mW Wireless Neural Recording IC With Spike Feature Extraction and UWB Transmitter

This paper reports a 128-channel neural recording integrated circuit (IC) with on-the-fly spike feature extraction and wireless telemetry. The chip consists of eight 16-channel front-end recording blocks, spike detection and feature extraction digital signal processor (DSP), ultra wideband (UWB) transmitter, and on-chip bias generators. Each recording channel has amplifiers with programmable gain and bandwidth to accommodate different types of biological signals. An analog-to-digital converter (ADC) shared by 16 amplifiers through time-multiplexing results in a balanced trade-off between the power consumption and chip area. A nonlinear energy operator (NEO) based spike detector is implemented for identifying spikes, which are further processed by a digital frequency-shaping filter. The computationally efficient spike detection and feature extraction algorithms attribute to an auspicious DSP implementation on-chip. UWB telemetry is designed to wirelessly transfer raw data from 128 recording channels at a data rate of 90 Mbit/s. The chip is realized in 0.35 mum complementary metal-oxide-semiconductor (CMOS) process with an area of 8.8 x 7.2 mm(2) and consumes 6 mW by employing a sequential turn-on architecture that selectively powers off idle analog circuit blocks. The chip has been tested for electrical specifications and verified in an ex vivo biological environment.

Performance evaluation of PCA-based spike sorting algorithms

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 Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.