A simple method for efficient spike detection in multiunit recordings

Currently, during the operation in shallow waters, scanning systems, such as multibeam systems, are capable of collecting thousands of points in a short time, promoting a greater coverage of the submerged bottom, with consequent increase in the detection capacity of objects. Although there has been an improvement in the accuracy of the depths collected, traditional processing, that is, manual, is still required. However, mainly due to the increased mass of data collected, manual processing has become extremely time-consuming and subjective, especially in the detection and elimination of spikes. Several algorithms are used to perform this task, but most of them are based on statistical assumptions hardly met and/or verified, such as spatial independence and normality. In this sense, the goal of this study is to present the SODA (Spatial Outlier Detection Algorithm) methodology, a new method for detection of spikes designed to treat bathymetric data collected through swath bathymetry systems. From computational simulation, promising results were obtained. SODA, in some cases, was capable to identify even 90% of spikes inserted on simulation, showing that the methodology is efficient and substantial to the bathymetric data treatment.

According to the characteristics of interferogram, we design a new spike detection method, which firstly filters an interferogram with two different ways and then detects spikes based on both results. Theoretical analysis and computer simulation shows that this algorithm performs well in detecting spikes in any position of an interferogram with high accuracy, and can be easily implemented in hardware.

Recordings of extracellular spikes have been widely used in various fields ranging from basic neuroscientific research to clinical applications. However, in the extracellular recording system, how to accurately detect spikes from the recorded signal in real time is still a major challenging work. Although the existing algorithms for online spike detection have made great progress, there still remains much room for improvement in terms of accuracy. In this paper, we propose a new method for high accuracy and real time spike detection. Concretely, differential operator is firstly employed to accentuate spikes in the signal for its simplicity and strong capacity to detect significant changes. Then, by exploiting the structural features of spikes, the resolution parameter is introduced to improve the performance of differential operator. Finally, a simple and effective measure is utilized to further reduce the influence of background noise, which makes spike detection more accurate. The results of simulated and real data show that the proposed method is able to precisely detect spikes while maintaining low computational complexity.

Detecting and classifying sharp transients in EEG (Electroencephalograph) recording by visual screening is a laborious and time-consuming task. That is why, there is an urgent need to construct a computer algorithm to detect automatically that type of EEG transient phenomena. The use of an artificial neural network as a classifier and wavelet analysis as pre-processing give promising results to answer that need. This paper proposes to develop a new method for the automatic detection of epileptic spikes based on Wavelet Neural Networks (WNN). A proper selection of scaling in WNN is introduced to overcome the problem of very long time duration during training. The result shows that proper selection of wavelet scaling can decrease training duration without decreasing WNN performance.

In spike sorting systems, front-end electronics is a crucial pre-processing step that not only has a direct impact on detection and sorting accuracy, but also on power and silicon area. In this work, a behavioural front-end model is proposed to assess the impact of the design parameters (including signal-to-noise ratio, filter type/order, bandwidth, converter resolution/rate) on subsequent spike processing. Initial validation of the model is provided by applying a test stimulus to a hardware platform and comparing the measured circuit response to the expected from the behavioural model. Our model is then used to demonstrate the effect of the Analogue Front-End (AFE) on subsequent spike processing by testing established spike detection and sorting methods on a selection of systems reported in the literature. It is revealed that although these designs have a wide variation in design parameters (and thus also circuit complexity), the ultimate impact on spike processing performance is relatively low (10-15%). This can be used to inform the design of future systems to have an efficient AFE whilst also maintaining good processing performance.

In this paper, we introduce a fully objective method to set thresholds (THs) for neuronal action potential spike detection from extracellular field potential signals. Although several more sophisticated methods exist, thresholding is still the most used spike detection method. In general, it is employed by setting a TH as per convention or operator decision, and without considering either the undetected or spurious spikes. Here, we demonstrate with both simulations and real microelectrode measurement data that our method can fully automatically and objectively yield THs comparable to those set by an expert operator. A Matlab function implementation of the method is described, and provided freely in Matlab Central File Exchange.

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also propagation of suprathreshold spiking activity involves populations of neurons. Empirical studies addressing cortical function directly thus require recordings from populations of neurons with high resolution. Here we describe an optical method and a deconvolution algorithm to record neural activity from up to 100 neurons with single-cell and single-spike resolution. This method relies on detection of the transient increases in intracellular somatic calcium concentration associated with suprathreshold electrical spikes (action potentials) in cortical neurons. High temporal resolution of the optical recordings is achieved by a fast random-access scanning technique using acousto-optical deflectors (AODs). Two-photon excitation of the calcium-sensitive dye results in high spatial resolution in opaque brain tissue. Reconstruction of spikes from the fluorescence calcium recordings is achieved by a maximum-likelihood method. Simultaneous electrophysiological and optical recordings indicate that our method reliably detects spikes (>97% spike detection efficiency), has a low rate of false positive spike detection (< 0.003 spikes/sec), and a high temporal precision (about 3 msec). This optical method of spike detection can be used to record neural activity in vitro and in anesthetized animals in vivo.

Developing electrophysiological recordings of brain neuronal activity and their analysis provide a basis for exploring the structure of brain function and nervous system investigation. The recorded signals are typically a combination of spikes and noise. High amounts of background noise and possibility of electric signaling recording from several neurons adjacent to the recording site have led scientists to develop neuronal signal processing tools such as spike sorting to facilitate brain data analysis. Spike sorting plays a pivotal role in understanding the electrophysiological activity of neuronal networks. This process prepares recorded data for interpretations of neurons interactions and understanding the overall structure of brain functions. Spike sorting consists of three steps: spike detection, feature extraction, and spike clustering. There are several methods to implement each of spike sorting steps. This paper provides a systematic comparison of various spike sorting sub-techniques applied to real extracellularly recorded data from a rat brain basolateral amygdala. An efficient sorted data resulted from careful choice of spike sorting sub-methods leads to better interpretation of the brain structures connectivity under different conditions, which is a very sensitive concept in diagnosis and treatment of neurological disorders. Here, spike detection is performed by appropriate choice of threshold level via three different approaches. Feature extraction is done through PCA and Kernel PCA methods, which Kernel PCA outperforms. We have applied four different algorithms for spike clustering including K-means, Fuzzy C-means, Bayesian and Fuzzy maximum likelihood estimation. As one requirement of most clustering algorithms, optimal number of clusters is achieved through validity indices for each method. Finally, the sorting results are evaluated using inter-spike interval histograms.

Spike sorting, i.e., the separation of the firing activity of different neurons from extracellular measurements, is a crucial but often error-prone step in the analysis of neuronal responses. Usually, three different problems have to be solved: the detection of spikes in the extracellular recordings, the estimation of the number of neurons and their prototypical (template) spike waveforms, and the assignment of individual spikes to those putative neurons. If the template spike waveforms are known, template matching can be used to solve the detection and classification problem. Here, we show that for the colored Gaussian noise case the optimal template matching is given by a form of linear filtering, which can be derived via linear discriminant analysis. This provides a Bayesian interpretation for the well-known matched filter output. Moreover, with this approach it is possible to compute a spike detection threshold analytically. The method can be implemented by a linear filter bank derived from the templates, and can be used for online spike sorting of multielectrode recordings. It may also be applicable to detection and classification problems of transient signals in general. Its application significantly decreases the error rate on two publicly available spike-sorting benchmark data sets in comparison to state-of-the-art template matching procedures. Finally, we explore the possibility to resolve overlapping spikes using the template matching outputs and show that they can be resolved with high accuracy.

Spike sorting is a fundamental process for decoding neural activity, involving preprocessing, spike detection, feature extraction, clustering, and validation. However, conventional spike sorting methods are highly fragmented, labor-intensive, and heavily reliant on expert manual curation, limiting their scalability and reproducibility. This challenge has become more pressing with advances in neural recording technology, such as high-density Neuropixels for large-scale neural recording or flexible electrodes for long-term stable recording over months to years. The volume and complexity of these datasets make manual curation infeasible, requiring an automated and scalable solution. Here, we introduce SpikeAgent, a multimodal large language model (LLM)-based AI agent that automates and standardizes the entire spike sorting pipeline. Unlike traditional approaches, SpikeAgent integrates multiple LLM backends, coding functions, and established algorithms, autonomously performing spike sorting with reasoning-based decision-making and real-time interaction with intermediate results. It generates interpretable reports, providing transparent justifications for each sorting decision, enhancing transparency and reliability. We benchmarked SpikeAgent against human experts across various neural recording technology, demonstrating its versatility and ability to achieve curation consistency that are equal to, or even higher than human experts. It also drastically reduces the expertise barrier and accelerates the curation and validation time by orders of magnitude. Moreover, it enables automated interpretability of the neural spiking data, which cannot be achieved by any conventional methods. SpikeAgent presents a paradigm shift in processing signals for neuroscience and brain-computer interfaces, while laying the ground for AI agent-augmented science across various domains.

In this research, we study the effect of feature selection in the spike detection and sorting accuracy. We introduce a new feature representation for neural spikes from multichannel recordings. The features selection plays a significant role in analyzing the response of brain neurons. The more precise selection of features leads to a more accurate spike sorting, which can group spikes more precisely into clusters based on the similarity of spikes. Proper spike sorting will enable the association between spikes and neurons. Different with other threshold-based methods, the cepstrum of spike signals is employed in our method to select the candidates of spike features. To choose the best features among different candidates, the Kolmogorov-Smirnov (KS) test is utilized. Then, we rely on the super paramagnetic method to cluster the neural spikes based on KS features. Simulation results demonstrate that the proposed method not only achieve more accurate clustering results but also reduce computational burden, which implies that it can be applied into real-time spike analysis.

Event Abstract Back to Event A unified open-source recording and online analysis software for deep brain recordings Simon Vogt1* and Ulrich G. Hofmann1 1 University of Lubeck, Germany With the increasing availability of high-speed deep brain recording systems for single-unit activity, each hardware manufacturer is trying to provide own proprietary software for controlling their deep brain recording hardware. While some of this recording software provides interesting additional features such as basic spike detection and clustering methods, most tools are either hard to learn or hard to extend. Instead of presenting a raw development framework for which the user needs programming skills before starting any experiments, we take an approach that allows any user to use it just minutes after downloading while also giving users with programming experience the opportunity to extend the efficient java-based code to their liking. A plug-in system allows the addition of new recording hardware and new analysis packages. Analysis plugins currently under active development include a statistical brain region identifier and automatic single unit clustering techniques. Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009. Presentation Type: Oral Presentation Topic: Electrophysiology Citation: Vogt S and Hofmann UG (2019). A unified open-source recording and online analysis software for deep brain recordings. Front. Neuroinform. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.061 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 22 May 2009; Published Online: 09 May 2019. * Correspondence: Simon Vogt, University of Lubeck, Lubeck, Germany, vogt@isip.uni-luebeck.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Simon Vogt Ulrich G Hofmann Google Simon Vogt Ulrich G Hofmann Google Scholar Simon Vogt Ulrich G Hofmann PubMed Simon Vogt Ulrich G Hofmann Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Sensory information is represented in the brain through the activity of populations of neurons. How this information is encoded and how it is processed and read out are crucial questions in neuroscience. The work presented here examines these issues using an insect brain model system. Specifically, this work addresses how odor information is represented across a population of neurons in this relatively simple nervous system. It asks how the dynamics of a population of neurons contribute to the encoding of information. To address these questions, simultaneous multi-unit extracellular recordings were made in vivo in the locust brain. The first part of the dissertation describes several advances in spike-sorting methods that were necessary for analyzing such recordings. These advances include quantitative tests of sorting quality, and they allow for automated spike-sorting. Using these techniques, data sampled from tens of neurons over hours of recording can be analyzed with relative ease. The remainder of the dissertation examines the encoding of olfactory information by a population of neurons called projection neurons (PNs), located in the first olfactory relay of the brain. Odor information is shown to be represented by a subpopulation of responsive PNs. The composition of this population changes over time in an odor-specific manner, thus forming a distributed, dynamical representation. The statistics of this response and its dynamics are quantified. Furthermore, the mechanism by which odor information is extracted from the PN population response is examined. A second set of recordings were made from Kenyon cells (KCs), which receive direct excitatory synaptic input from PNs. The dynamic response of the PN population appears to be decoded by KCs through a mechanism based on several underlying components, including oscillatory dynamics, feed-forward inhibition, and intrinsic properties of the KCs. This decoding process is shown to drastically change the odor representations, from dense to sparse. Taken together, the results presented in this dissertation establish that the complex spatial and temporal dynamics of the PN population do encode odor information, and that this information is decoded by other neurons (KCs) in a very precise way, resulting in a drastic transformation of representation. The basic mechanisms underlying this transformation exist in many brain areas and across phyla, suggesting that many of the principles described here could be of general relevance.

This thesis was to solve the problem that tremendous amount of data of neural signal seriously confined the number of transmission channels with a limited bandwidth in a digital telemetry system. Although a spike-detection algorithm designed for data reduction had been reported, the signal in between the spikes was lost. Moreover, various non-ideal factors greatly reduced the accuracy of spike detection. This thesis used the commercialized data-compression algorithm, MPEG Layer 3 (MP3), as our data reduction scheme and evaluated its performance on spike sorting. The evaluation results showed that MP3 kept at least 80% accuracy of unit isolation. For reducing the consumption of hardware resource by MP3, MP3 algorithm was further simplified and the corresponding performance was checked. All the algorithm simplification carried out in this thesis compromised barely discernible performance. At last this thesis discussed a method for multi-channel signal analysis, ICA. The effect of MP3 on ICA after the highest level of simplification was simulated. The results showed that the miss probabilities of MP3 were considerably low, regardless of the encoding bit rate. According to all the results mentioned above, we preliminarily validated MP3 a proper neural signal compressor.

Performance metrics for the accurate characterisation of interictal spike detection algorithms