Spike Detection Research Articles

Turning Noise Into Data: Characterization of the Van Allen Radiation Belt Using SDO Spikes Data

The Solar Dynamics Observatory (SDO) is a solar mission in an inclined geosynchronous orbit. Since commissioning, images acquired by Atmospheric Imaging Assembly (AIA) instrument on-board the SDO have frequently displayed “spikes,” pixel regions yielding extreme number of digital counts. These are theorized to occur from energetic electron collisions with the instrument detector system. These spikes are regularly removed from AIA Level 1.0 images to produce clean and reliable data. A study of historical data has found over 100 trillion spikes in the past decade. This project correlates spike detection frequency with radiation environment parameters in order to generate an augmented data product from SDO. We conduct a correlation study between SDO/AIA data and radiation belt activity within the SDO's orbit. By extracting radiation “spike” data from the SDO/AIA images, we produce a comprehensive data product which is correlated not only with geomagnetic parameters such as Kp, Ap, and Sym-H but also with the electron and proton fluxes measured by the GOES-14 satellite. As a result, we find that AIA spikes are highly correlated with the GOES-14 electrons detected by the magnetospheric electron detector and energetic proton, electron and alpha detectors instruments at the equator (where the two satellites meet) with Spearman's Correlation values of ρ = 0.73 and ρ = 0.53, respectively, while a weaker correlation of ρ = 0.47 is shown with magnetospheric proton detector protons for the 2 year period where both missions returned data uninterruptedly. This correlation proves that the SDO spike data can be proven useful for characterizing the Van Allen radiation belt, especially at areas where other satellites cannot.

Sensor selection and miniaturization limits for detection of interictal epileptiform discharges with wearable EEG

Objective. The goal of this paper is to investigate the limits of electroencephalography (EEG) sensor miniaturization in a set-up consisting of multiple galvanically isolated EEG units to record interictal epileptiform discharges (IEDs), referred to as ‘spikes’, in people with epilepsy. Approach. A dataset of high-density EEG recordings (257 channels) was used to emulate local EEG sensor units with short inter-electrode distances. A computationally efficient sensor selection and interictal spike detection algorithm was developed and used to assess the influence of the inter-electrode distance and the number of such EEG units on spike detection performance. Signal-to-noise ratio, correlation with a clinical-grade IEDs detector and Cohen’s kappa coefficient of agreement were used to quantify performance. Bayesian statistics were used to confirm the statistical significance of the observed results. Main results. We found that EEG recording equipment should be specifically designed to measure the small signal power at short inter-electrode distance by providing an input referred noise 300 nV. We also found that an inter-electrode distance of minimum 5 cm between electrodes in a setup with a minimum of two EEG units is required to obtain near equivalent performance in interictal spike detection to standard EEG. Significance. These findings provide design guidelines for miniaturizing EEG systems for long term ambulatory monitoring of interictal spikes in epilepsy patients.

Power-efficient in vivo brain-machine interfaces via brain-state estimation

Objective. Advances in brain–machine interfaces (BMIs) can potentially improve the quality of life of millions of users with spinal cord injury or other neurological disorders by allowing them to interact with the physical environment at their will. Approach. To reduce the power consumption of the brain-implanted interface, this article presents the first hardware realization of an in vivo intention-aware interface via brain-state estimation. Main Results. It is shown that incorporating brain-state estimation reduces the in vivo power consumption and reduces total energy dissipation by over 1.8× compared to those of the current systems, enabling longer better life for implanted circuits. The synthesized application-specific integrated circuit (ASIC) of the designed intention-aware multi-unit spike detection system in a standard 180 nm CMOS process occupies 0.03 mm2 of silicon area and consumes 0.63 µW of power per channel, which is the least power consumption among the current in vivo ASIC realizations. Significance. The proposed interface is the first practical approach towards realizing asynchronous BMIs while reducing the power consumption of the BMI interface and enhancing neural decoding performance compared to those of the conventional synchronous BMIs.

On-FPGA Spiking Neural Networks for End-to-End Neural Decoding

In the last decades, deep learning neural decoding algorithms have gained momentum in the field of neural interfaces and neural processing systems. However, to be deployed on low-budget portable devices while maintaining real-time operability, these models must withstand strict computational and power limitations. This work presents a spike decoding system implemented on a low-end Zynq-7010 FPGA, which includes a multiplier-less spike detection pipeline and a spiking-neural-network-based decoder mapped in the programmable logic. We tested the system on two publicly available datasets and achieved comparable results with state-of-the-art neural decoders that use more complex deep learning models. The system required 7.36 times fewer parameters than the smallest architecture tested on the same dataset. Moreover, by exploiting the spike sparsity property of the neural signal, the total amount of computations is reduced by about 90% during a test carried out on real recorded data. The low computational complexity of the chosen spike detection setup, combined with the power efficiency of spiking neural networks, makes this prototype a well-suited choice for low-power real-time neural decoding at the edge.

A Deep Neural Network-Based Spike Sorting With Improved Channel Selection and Artefact Removal

In order to implement highly efficient brain-machine interface (BMI) systems, high-channel count sensing is often used to record extracellular action potentials. However, the extracellular recordings are typically severely contaminated by artefacts and various noise sources, rendering the separation of multi-unit neural recordings an immensely challenging task. Removing artefact and noise from neural events can improve the spike sorting performance and classification accuracy. This paper presents a deep learning technique called deep spike detection (DSD) with a strong learning ability of high-dimensional vectors for neural channel selection and artefacts removal from the selected neural channel. The proposed method significantly improves spike detection compared to the conventional methods by sequentially diminishing the noise level and discarding the active artefacts in the recording channels. The simulated and experimental results show that there is considerably better performance when the extracellular raw recordings are cleaned prior to assigning individual spikes to the neurons that generated them. The DSD achieves an overall classification accuracy of 91.53% and outperformes Wave_clus by 3.38% on the simulated dataset with various noise levels and artefacts.

Fast Detection and Quantification of Interictal Spikes and Seizures in a Rodent Model of Epilepsy Using an Automated Algorithm.

The electroencephalogram (EEG) is a powerful tool for analyzing neural activity in various neurological disorders, both in animals and in humans. This technology has enabled researchers to record the brain's abrupt changes in electrical activity with high resolution, thus facilitating efforts to understand the brain's response to internal and external stimuli. The EEG signal acquired from implanted electrodes can be used to precisely study the spiking patterns that occur during abnormal neural discharges. These patterns can be analyzed in conjunction with behavioral observations and serve as an important means for accurate asses sment and quantification of behavioral and electrographic seizures. Numerous algorithms have been developed for the automated quantification of EEG data; however, many of these algorithms were developed with outdated programming languages and require robust computational hardware to run effectively. Additionally, some of these programs require substantial computation time, reducing the relative benefits of automation. Thus, we sought to develop an automated EEG algorithm that was programmed using a familiar programming language (MATLAB), and that could run efficiently without extensive computational demands. This algorithm was developed to quantify interictal spikes and seizures in mice that were subjected to traumatic brain injury. Although the algorithm was designed to be fully automated, it can be operated manually, and all the parameters for EEG activity detection can be easily modified for broad data analysis. Additionally, the algorithm is capable of processing months of lengthy EEG datasets in the order of minutes to hours, reducing both analysis time and errors introduced through manual-based processing.

A Novel and Efficient Surface Electromyography Decomposition Algorithm Using Local Spatial Information.

Motor unit spike trains (MUSTs) decomposed from surface electromyography (sEMG) have been an emerging solution for neural interfacing, especially for the control of upper limb prosthetics. Accurate and efficient decomposition techniques are essential and desirable. However, most decomposition methods are designed for motor units (MUs) with global maximum of single or large muscle, while in general forearm muscles are usually small and slender with low global energy. Thus, we propose a novel approach using local spatial information towards more accurate and efficient sEMG decomposition of forearm muscles. A fast spatial spike detection method is proposed to replace the time-consuming iteration process of blind source separation (BSS) methods. Here, spatial distribution characteristics of motor unit action potential are leveraged to pre-classify the candidate MUs, and further to create initial MU templates, aiming to avoid repeating convergence to high-energy MUs. The results of both simulated and experimental sEMG signals show that low-energy MUs from small muscles are more easily found compared with conventional BSS algorithm. Specifically, the proposed method can identify more 40% reliable MUs while only 30% consuming time are needed. The outcomes provide a novel solution for more efficient sEMG decomposition, potentially paving the way of MUST-based non-invasive neural interface.

Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet.

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.

A Subpopulation of Spikes Predicts Successful Epilepsy Surgery Outcome.

Epileptic spikes are the traditional interictal electroencephalographic (EEG) biomarker for epilepsy. Given their low specificity for identifying the epileptogenic zone (EZ), they are given only moderate attention in presurgical evaluation. This study aims to demonstrate that it is possible to identify specific spike features in intracranial EEG that optimally define the EZ and predict surgical outcome. We analyzed spike features on stereo-EEG segments from 83 operated patients from 2 epilepsy centers (37 Engel IA) in wakefulness, non-rapid eye movement sleep, and rapid eye movement sleep. After automated spike detection, we investigated 135 spike features based on rate, morphology, propagation, and energy to determine the best feature or feature combination to discriminate the EZ in seizure-free and non-seizure-free patients by applying 4-fold cross-validation. The rate of spikes with preceding gamma activity in wakefulness performed better for surgical outcome classification (4-fold area under receiver operating characteristics curve [AUC] = 0.755 ± 0.07) than the seizure onset zone, the current gold standard (AUC=0.563 ± 0.05, p= 0.015) and the ripple rate, an emerging seizure-independent biomarker (AUC=0.537 ± 0.07, p= 0.006). Channels with a spike-gamma rate exceeding 1.9/min had an 80% probability of being in the EZ. Combining features did not improve the results. Resection of brain regions with high spike-gamma rates in wakefulness is associated with a high probability of achieving seizure freedom. This rate could be applied to determine the minimal number of spiking channels requiring resection. In addition to quantitative analysis, this feature is easily accessible to visual analysis, which could aid clinicians during presurgical evaluation. ANN NEUROL 2023;93:522-535.

Advances in epilepsy monitoring by detection and analysis of brain epileptiform discharges.

Brain interictal and pre-ictal epileptiform discharges (EDs) are transient events occurred between two or before seizure onsets visible in intracranial electroencephalographs. In the diagnosis of epilepsy and localization of seizure sources, both interictal and ictal recordings are extremely informative. For this propose, computerized intelligent spike and seizure detection techniques have been researched and are constantly improving. This is not only to detect more EDs from over the scalp but also to classify epileptic and non-epileptic discharges. Tensor factorization and deep learning are two advanced and powerful techniques which have been recently suggested for ED detection. Here, our main contribution is to review recent ED detection methods with emphasis on multi-way analysis and deep learning approaches. These techniques have opened a new window to the epilepsy diagnosis and management spheres.

Machine learning‐enabled epileptiform spike detection stratifies patient subpopulations with Alzheimer’s disease

AbstractBackgroundA subpopulation of patients with Alzheimer’s disease exhibit epileptiform activity and have more rapidly progressing disease. Manual identification of these pathological events is infeasible at scale and machine learning tools may establish epileptiform activity as an actionable biomarker for stratification and endpoints within clinical trials.MethodsRoutine EEGs from 90 subjects with AD and 39 subjects with MCI from the Beacon data repository were analyzed via the Beacon Platform harnessing an ML‐enabled interictal epileptiform discharge (IED) detection algorithm, and by expert visual inspection.ResultsIEDs were found in 17 of the 90 subjects with AD (19%), and 8 out of the 39 MCI subjects (20%). In subcategorization, 13% (9 / 71) of AD subjects without epilepsy had IEDs, while 42% (8/19) of AD subjects with epilepsy had IEDs. In the AD/no epilepsy subgroup there were 24 patients taking an Anti‐Seizure Medication (ASM), and 4% (1/24) had IEDs (versus 17% (8/47) of the AD without epilepsy group that were not on ASMs).ConclusionMachine learning augmented detection of IEDs allows for rapid, quantitative identification of individuals with subclinical epileptiform activity in patients with AD and MCI. These results demonstrate that AI‐detectable IEDs are seen in a sizable minority of AD patients without epilepsy and in MCI patients, and more commonly in AD patients with epilepsy. Ultimately, automated analysis of EEG data is a promising way to stratify patients and track disease progression while enriching clinical trials with subjects most likely to respond to next‐generation treatments.

BECT Spike Detection Based on Novel Multichannel Data Weighted Fusion Algorithm

Benign epilepsy with spinous waves in the central temporal region (BECT) is the most common epilepsy syndromes in children. Spike discharges in the Rolandic area are important biomarkers for diagnosis evaluation. Conventional single-channel electroencephalogram (EEG) based spike detection methods are generally susceptible to artifact interference. To address this issue, a novel spike detection method based on multichannel EEG weighted fusion strategy is developed in this brief. The proposed algorithm mainly includes multichannel spike candidate sample screening, data weighted fusion, time-series feature extraction and long-short-time memory neural networks (LSTM) detection. Studies on 15 BECT children show that the proposed algorithm can obtain an average of 95.74% F1 scores, 93.94% sensitivity, 97.73% precision for all subjects.

Yield-SpikeSegNet: An Extension of SpikeSegNet Deep-Learning Approach for the Yield Estimation in the Wheat Using Visual Images

ABSTRACT High-throughput plant phenotyping integrated with computer vision is an emerging topic in the domain of nondestructive and noninvasive plant breeding. Analysis of the emerging grain spikes and the grain weight or yield estimation in the wheat plant for a huge number of genotypes in a nondestructive way has achieved significant research attention. In this study, we developed a deep learning approach, “Yield-SpikeSegNet,” for the yield estimation in the wheat plant using visual images. Our approach consists of two consecutive modules: “Spike detection module” and “Yield estimation module.” The spike detection module is implemented using a deep encoder-decoder network for spike segmentation and output of this module is spike area and spike count. In yield estimation module, we develop machine learning models using artificial neural network and support vector regression for the yield estimation in the wheat plant. The model’s precision, accuracy, and robustness are found satisfactory in spike segmentation as 0.9982, 0.9987, and 0.9992, respectively. The spike segmentation and yield estimation performance reflect that the Yield-SpikeSegNet approach is a significant step forward in the domain of high-throughput and nondestructive wheat phenotyping.

Data-driven approach for the delineation of the irritative zone in epilepsy in MEG.

The reliable identification of the irritative zone (IZ) is a prerequisite for the correct clinical evaluation of medically refractory patients affected by epilepsy. Given the complexity of MEG data, visual analysis of epileptiform neurophysiological activity is highly time consuming and might leave clinically relevant information undetected. We recorded and analyzed the interictal activity from seven patients affected by epilepsy (Vectorview Neuromag), who successfully underwent epilepsy surgery (Engel > = II). We visually marked and localized characteristic epileptiform activity (VIS). We implemented a two-stage pipeline for the detection of interictal spikes and the delineation of the IZ. First, we detected candidate events from peaky ICA components, and then clustered events around spatio-temporal patterns identified by convolutional sparse coding. We used the average of clustered events to create IZ maps computed at the amplitude peak (PEAK), and at the 50% of the peak ascending slope (SLOPE). We validated our approach by computing the distance of the estimated IZ (VIS, SLOPE and PEAK) from the border of the surgically resected area (RA). We identified 25 spatiotemporal patterns mimicking the underlying interictal activity (3.6 clusters/patient). Each cluster was populated on average by 22.1 [15.0-31.0] spikes. The predicted IZ maps had an average distance from the resection margin of 8.4 ± 9.3 mm for visual analysis, 12.0 ± 16.5 mm for SLOPE and 22.7 ±. 16.4 mm for PEAK. The consideration of the source spread at the ascending slope provided an IZ closer to RA and resembled the analysis of an expert observer. We validated here the performance of a data-driven approach for the automated detection of interictal spikes and delineation of the IZ. This computational framework provides the basis for reproducible and bias-free analysis of MEG recordings in epilepsy.

A wheat spike detection method based on Transformer.

Wheat spike detection has important research significance for production estimation and crop field management. With the development of deep learning-based algorithms, researchers tend to solve the detection task by convolutional neural networks (CNNs). However, traditional CNNs equip with the inductive bias of locality and scale-invariance, which makes it hard to extract global and long-range dependency. In this paper, we propose a Transformer-based network named Multi-Window Swin Transformer (MW-Swin Transformer). Technically, MW-Swin Transformer introduces the ability of feature pyramid network to extract multi-scale features and inherits the characteristic of Swin Transformer that performs self-attention mechanism by window strategy. Moreover, bounding box regression is a crucial step in detection. We propose a Wheat Intersection over Union loss by incorporating the Euclidean distance, area overlapping, and aspect ratio, thereby leading to better detection accuracy. We merge the proposed network and regression loss into a popular detection architecture,fully convolutional one-stage object detection, and name the unified model WheatFormer. Finally, we construct a wheat spike detection dataset (WSD-2022) to evaluate the performance of the proposed methods. The experimental results show that the proposed network outperforms those state-of-the-art algorithms with 0.459 mAP (mean average precision) and 0.918 AP50. It has been proved that our Transformer-based method is effective to handle wheat spike detection under complex field conditions.

Using Split Luminescent Biosensors for SARS-CoV-2 Antibody Detection in Serum, Plasma, and Blood Samples.

Antibody detection assays are essential for evaluating immunity of individuals against a given virus, and this has been particularly relevant during the COVID-19 pandemic. Current serology assays either require a laboratory setting and take >1 hr (i.e., enzyme-linked immunosorbent assay [ELISA]) or are rapid but only qualitative in nature and cannot accurately track antibody levels over time (i.e., lateral flow assay [LFA]). Therefore, there is a need for development of a rapid and simple but also quantitative assay that can evaluate antibody levels in patients accurately over time. We have developed an assay that uses a split nanoluciferase fused to the spike or nucleocapsid proteins of the SARS-CoV-2 virus to enable luminescent-based detection of spike- or nucleocapsid-binding antibodies in serum, plasma, and whole blood samples. The resulting approach is simple, rapid, and quantitative and is highly amenable to low-/medium-throughput scale using plate-based assays, high-throughput scale using robotics, and point-of-care applications. In this article, we describe how to perform the assay in a laboratory setting using a plate reader or liquid-handling robotics and in a point-of-care setting using a handheld, battery-powered luminometer. Together, these assays allow antibody detection to be easily performed in multiple settings by simplifying and reducing assay time in a laboratory or clinical environment and by allowing for antibody detection in point-of-care, nonlaboratory settings. © 2022 Wiley Periodicals LLC. Basic Protocol: SARS-CoV-2 antibody detection using the split-luciferase assay on a medium-throughput scale with a laboratory luminometer Alternate Protocol 1: High-throughput-based protocol for SARS-CoV-2 antibody detection using a robotic platform Alternate Protocol 2: Point-of-care-based protocol for SARS-CoV-2 antibody detection using a handheld luminometer Support Protocol: Determining positive/negative cutoffs for test samples and standardizing the assay between days.

Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning.

Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, these analyses are manually performed by neurophysiologists and are time-consuming. Another problem is that spike identification from MEG waveforms largely depends on neurophysiologists' skills and experiences. These problems cause poor cost-effectiveness in clinical MEG examination. To overcome these problems, we fully automated spike identification and ECD estimation using a deep learning approach fully automated AI-based MEG interictal epileptiform discharge identification and ECD estimation (FAMED). We applied a semantic segmentation method, which is an image processing technique, to identify the appropriate times between spike onset and peak and to select appropriate sensors for ECD estimation. FAMED was trained and evaluated using clinical MEG data acquired from 375 patients. FAMED training was performed in two stages: in the first stage, a classification network was learned, and in the second stage, a segmentation network that extended the classification network was learned. The classification network had a mean AUC of 0.9868 (10-fold patient-wise cross-validation); the sensitivity and specificity were 0.7952 and 0.9971, respectively. The median distance between the ECDs estimated by the neurophysiologists and those using FAMED was 0.63 cm. Thus, the performance of FAMED is comparable to that of neurophysiologists, and it can contribute to the efficiency and consistency of MEG ECD analysis.

Satelight: self-attention-based model for epileptic spike detection from multi-electrode EEG

Objective. Because of the lack of highly skilled experts, automated technologies that support electroencephalogram (EEG)-based in epilepsy diagnosis are advancing. Deep convolutional neural network-based models have been used successfully for detecting epileptic spikes, one of the biomarkers, from EEG. However, a sizeable number of supervised EEG records are required for training. Approach. This study introduces the Satelight model, which uses the self-attention (SA) mechanism. The model was trained using a clinical EEG dataset labeled by five specialists, including 16 008 epileptic spikes and 15 478 artifacts from 50 children. The SA mechanism is expected to reduce the number of parameters and efficiently extract features from a small amount of EEG data. To validate the effectiveness, we compared various spike detection approaches with the clinical EEG data. Main results. The experimental results showed that the proposed method detected epileptic spikes more effectively than other models (accuracy = 0.876 and false positive rate = 0.133). Significance. The proposed model had only one-tenth the number of parameters as the other effective model, despite having such a high detection performance. Further exploration of the hidden parameters revealed that the model automatically attended to the EEG’s characteristic waveform locations of interest.

Environmental surveillance and clinical assessment of SARS-CoV-2 epidemiology and variant prevalence in an urban area in Spain

BACKGROUND AND AIM: Wastewater-based epidemiology (WBE) provides the means to monitor pathogens prevalence in the sewage system of a community. SARS-CoV-2, the virus responsible for the COVID-19 global pandemic, can be detected in the feces of infected individuals. Therefore, viral concentrations in wastewater can be taken as an indication of SARS-CoV-2 prevalence in the entire population. This work was aimed at the detection and sequence analysis of SARS-CoV-2 RNA in wastewater and its correlation with clinical data. METHODS: Samples collected over a nine-month period (2020-21) at a wastewater treatment plant in Elche (Spain) were concentrated by ultrafiltration and analyzed for the presence of SARS-CoV-2 RNA by reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). Sequences generated from cDNA samples with a MinION Nanopore device were compared to SARS-CoV-2 sequence databases. RESULTS: The appearance of the first qPCR positive signals correlated with the increasing number of covid-19 cases in Elche at the onset of the second wave of COVID-19 (july 2020). Measurements of viral genomes in water samples in October-November 2020 reflected the higher number of clinical cases within that period. Nanopore sequencing assay allowed the detection of spike (S) protein mutations associated with circulating SARS-CoV-2 variants both in environmental as well as in clinical samples, reflecting the emergence of the B.1.1.7 variant in Elche in late 2020. CONCLUSIONS: This study highligted the potential of wastewater-based epidemiology (WBE) in revealing the presence of pathogenic viruses in urban populations. By the use of the nanopore sequencing technology we were able to assess the changes in circulating SARS-CoV-2 variants in a whole population. KEYWORDS: environmental epidemiology, SARS-CoV-2 variants, wastewater, nanopore sequencing

Low-Power Energy-Based Spike Detector ASIC for Implantable Multichannel BMIs

Advances in microtechnology have enabled an exponential increase in the number of neurons that can be simultaneously recorded. To meet high-channel count and implantability demands, emerging applications require new methods for local real-time processing to reduce the data to transmit. Nonlinear energy operators are widely used to distinguish neural spikes from background noise featuring a good tradeoff between hardware resources and accuracy. However, they require an additional smoothing filter, which affects both area occupation and power dissipation. In this paper, we investigate a spike detector, based on a series of two nonlinear energy operators, and a simple and adaptive threshold, based on a three-point median operator. We show that our proposal provides good accuracy compared to other energy-based detectors on a synthetic dataset at different noise levels. Based on the proposed technique, a 1024-channel neural signal processor was designed in a 28 nm TSMC CMOS process by using latch-based static random-access memory (SRAM), demonstrating a total power consumption of 1.4 μW/ch and a silicon area occupation of 230 μm2/ch. These features, together with a comparison with the state of the art, demonstrate that our proposal constitutes an alternative for the development of next-generation multichannel neural interfaces.