Spike Detection Research Articles

Prototype of Monitoring Transportation Pollution Spikes through the Internet of Things Edge Networks

Air pollution is a critical problem in densely populated urban areas, with traffic significantly contributing. To mitigate the adverse effects of air pollution on public health and the environment, there is a growing need for the real-time monitoring and detection of pollution spikes in transportation. This paper presents a novel approach to using Internet of Things (IoT) edge networks for the real-time detection of air pollution peaks in transportation, specifically designed for innovative city applications. The proposed system uses IoT sensors in buses, cabs, and private cars. These sensors are equipped with air quality monitoring capabilities, including the measurement of pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and carbon dioxide (CO2). The sensors continuously collect air quality data and transmit them to edge devices within the transportation infrastructure. The data collected by these sensors are analyzed, and alerts are generated when pollution levels exceed predefined thresholds. By deploying this system within IoT edge networks, transportation authorities can promptly respond to pollution spikes, improving air quality, public health, and environmental sustainability. This paper details the sensor technology, data analysis methods, and the practical implementation of this innovative system, shedding light on its potential for addressing the pressing issue of transportation-related pollution. The proposed IoT edge network for real-time air pollution spike detection in transportation offers significant advantages, including low-latency data processing, scalability, and cost-effectiveness. By leveraging the power of edge computing and IoT technologies, smart cities can proactively monitor and manage air pollution, leading to healthier and more sustainable urban environments.

Multichannel Electrophysiological Recording With Spike Detection and Sorting in a Duty-Cycled Wireless Optogenetic Headstage

Multichannel Electrophysiological Recording With Spike Detection and Sorting in a Duty-Cycled Wireless Optogenetic Headstage

Cumulative Spike Train Estimation for Muscle Excitation Assessment From Surface EMG Using Spatial Spike Detection.

Estimating cumulative spike train (CST) of motor units (MUs) from surface electromyography (sEMG) is essential for the effective control of neural interfaces. However, the limited accuracy of existing estimation methods greatly hinders the further development of neural interface. This paper proposes a simple but effective approach for identifying CST based on spatial spike detection from high-density sEMG. Specifically, we use a spatial sliding window to detect spikes according to the spatial propagation characteristics of the motor unit action potential, focusing on the spikes of activated MUs in a local area rather than those of a specific MU. We validated the effectiveness of our proposed method through an experiment involving wrist flexion/extension and pronation/supination, comparing it with a recognized CST estimation method and an MU decomposition based method. The results demonstrated that the proposed method obtained higher accuracy on multi-DoF wrist torque estimation leveraging the estimated CST compared to the other three methods. On average, the correlation coefficient (R) and the normalized root mean square error (nRMSE) between the estimation results and recorded force were 0.96 0.03 and 10.1% 3.7%, respectively. Moreover, there was an extremely high interpretive extent between the CSTs of proposed method and the MU decomposition method. The outcomes reveal the superiority of the proposed method in identifying CSTs and can provide promising driven signals for neural interface.

Design of a 5.94 FO4 absolute-value detector based on hybrid logic technique

With the development of neural signal acquisition systems, spike sorting algorithms have become a hot topic in related fields. The delay of the spike detection system largely determines the speed of the overall signal acquisition system. This paper designs a low-delay Absolute-value Detector based on the GDI technique to improve the timeliness of the neural signal acquisition system. The final circuit has only 20 logic gates-56 transistors. According to the logical effort methodology, the final path delay is 5.94 FO4. This research can provide a reference for the design of future portable ultra-low delay neural signal acquisition systems.

Comparison of Automated Spike Detection Software in Detecting Epileptiform Abnormalities on Scalp-EEG of Genetic Generalized Epilepsy Patients.

Despite availability of commercial EEG software for automated epileptiform detection, validation on real-world EEG datasets is lacking. Performance evaluation of two software packages on a large EEG dataset of patients with genetic generalized epilepsy was performed. Three epileptologists labelled IEDs manually of EEGs from three centres. All Interictal epileptiform discharge (IED) markings predicted by two commercial software (Encevis 1.11 and Persyst 14) were reviewed individually to assess for suspicious missed markings and were integrated into the reference standard if overlooked during manual annotation during a second phase. Sensitivity, precision, specificity, and F1-score were used to assess the performance of the software packages against the adjusted reference standard. One hundred and twenty-five routine scalp EEG recordings from different subjects were included (total recording time, 310.7 hours). The total epileptiform discharge reference count was 5,907 (including spikes and fragments). Encevis demonstrated a mean sensitivity for detection of IEDs of 0.46 (SD 0.32), mean precision of 0.37 (SD 0.31), and mean F1-score of 0.43 (SD 0.23). Using the default medium setting, the sensitivity of Persyst was 0.67 (SD 0.31), with a precision of 0.49 (SD 0.33) and F1-score of 0.51 (SD 0.25). Mean specificity representing non-IED window identification and classification was 0.973 (SD 0.08) for Encevis and 0.968 (SD 0.07) for Persyst. Automated software shows a high degree of specificity for detection of nonepileptiform background. Sensitivity and precision for IED detection is lower, but may be acceptable for initial screening in the clinical and research setting. Clinical caution and continuous expert human oversight are recommended with all EEG recordings before a diagnostic interpretation is provided based on the output of the software.

Application of concanavalin A as a new diagnostic strategy for SARS-COV-2 spike protein

The coronavirus disease 2019 (COVID-19) pandemic was the first modern global public health crisis recognized for overwhelming the health public system. In the viral envelope, there are glycoproteins that can be identified, making them useful in diagnosing infections such as the one caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this work, we developed a sensor platform applicable to the identification of carbohydrates present in the spike protein of SARS-CoV-2. The nanostructured platform was composed of a thin film of poly (thiophene-3-acetic acid) (PTAA) and nano gold urchins (AuNUs), enhancing the electrical signal at the interface of the gold electrode. Coupled to this nanostructured layer, the lectin Concanavalin A (ConA) was immobilized, which has binding affinity to mannose and N-acetylglucosamine residues. In this context, ConA was used as a biorecognition element against Spike protein carbohydrates. Gradual assembly of the sensor platform and recombinant viral target recognition were evaluated by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and atomic force microscopy (AFM). The proposed biosensor showed high sensitivity, with a limit of detection (LOD) of Spike isolated 0.20 ng/µL and Spike serum 0.14 ng/µL, along with high selectivity against interfering molecules. Our results stand out as an innovative alternative for detecting viral glycoproteins, with promising applications in detecting SARS-CoV-2 in clinical samples.

DualSort: online spike sorting with a running neural network

Objective. Spike sorting, i.e. the detection and separation of measured action potentials from different extracellularly recorded neurons, remains one of the bottlenecks in deciphering the brain. In recent years, the application of neural networks (NNs) for spike sorting has garnered significant attention. Most methods focus on specific sub-problems within the conventional spike sorting pipeline, such as spike detection or feature extraction, and attempt to solve them with complex network architectures. This paper presents DualSort, a simple NN that gets combined with downstream post-processing for real-time spike sorting. It shows high efficiency, low complexity, and requires a comparatively small amount of human interaction. Approach. Synthetic and experimentally obtained extracellular single-channel recordings were utilized to train and evaluate the proposed NN. For training, spike waveforms were labeled with respect to their associated neuron and position in the signal, allowing the detection and categorization of spikes in unison. DualSort classifies a single spike multiple times in succession, as it runs over the signal in a step-by-step manner and uses a post-processing algorithm that transmits the network output into spike trains. Main results. With the used datasets, DualSort was able to detect and distinguish different spike waveforms and separate them from background activity. The post-processing algorithm significantly strengthened the overall performance of the model, making the system more robust as a whole. Although DualSort is an end-to-end solution that efficiently transforms filtered signals into spike trains, it competes with contemporary state-of-the-art technologies that exclusively target single sub-problems in the conventional spike sorting pipeline. Significance. This work demonstrates that even under high noise levels, complex NNs are not necessary by any means to achieve high performance in spike detection and sorting. The utilization of data augmentation on a limited quantity of spikes could substantially decrease hand-labeling compared to other studies. Furthermore, the proposed framework can be utilized without human interaction when combined with an unsupervised technique that provides pseudo labels for DualSort. Due to the low complexity of our network, it works efficiently and enables real-time processing on basic hardware. The proposed approach is not limited to spike sorting, as it may also be used to process different signals, such as electroencephalogram (EEG), which needs to be investigated in future research.

Graph neural networks in EEG spike detection

Objective:This study develops new machine learning architectures that are more adept at detecting interictal epileptiform discharges (IEDs) in scalp EEG. A comparison of results using the average precision (AP) metric is made with the proposed models on two datasets obtained from Baptist Hospital of Miami and Temple University Hospital. Methods:Applying graph neural networks (GNNs) on functional connectivity (FC) maps of different frequency sub-bands to yield a novel architecture we call FC-GNN. Attention mechanism is applied on a complete graph to let the neural network select its important edges, hence bypassing the extraction of features, a model we refer to as CA-GNN. Results:On the Baptist Hospital dataset, the results were as follows: Vanilla Self-Attention →0.9029±0.0431, Hierarchical Attention →0.8546±0.0587, Vanilla Visual Geometry Group (VGG) →0.92±0.0618, Satelight →0.9219±0.046, FC-GNN →0.9731±0.0187, and CA-GNN →0.9788±0.0125. In the same order, the results on the Temple University Hospital dataset are 0.9692, 0.9113, 0.97, 0.9575, 0.963, and 0.9879. Conclusion:Based on the good results they yield, GNNs prove to have a strong potential in detecting epileptogenic activity. Significance:This study opens the door for the discovery of the powerful role played by GNNs in capturing IEDs, which is an essential step for identifying the epileptogenic networks of the affected brain and hence improving the prospects for more accurate 3D source localization.

Limitations of Pacemaker Spike Detection in Capacitive ECGs via Deep Learning

Abstract Pacemaker spike detection is an important step in monitoring paced patients. Capacitive ECG facilitates unobtrusive monitoring of subjects during daily routines such as driving. Robust algorithms are required to deal with low signal quality and artifacts, e.g. by employing fusion of multiple signal channels. Due to the low signal-to-noise ratio of the measurement, there are limitations to detection accuracy compared to conventional ECG monitors. Especially low voltage stimulations such as bipolar pacemaker spikes are hard to detect. We present a convolutional network approach to improve on recent signal processing algorithms.We show a realistic evaluation of its performance using leave-one-subject-out cross validation (LOOCV), its dependence on the size of the receptive field, and an estimation of an upper performance bound.

WheatSpikeNet: an improved wheat spike segmentation model for accurate estimation from field imaging.

Phenotyping is used in plant breeding to identify genotypes with desirable characteristics, such as drought tolerance, disease resistance, and high-yield potentials. It may also be used to evaluate the effect of environmental circumstances, such as drought, heat, and salt, on plant growth and development. Wheat spike density measure is one of the most important agronomic factors relating to wheat phenotyping. Nonetheless, due to the diversity of wheat field environments, fast and accurate identification for counting wheat spikes remains one of the challenges. This study proposes a meticulously curated and annotated dataset, named as SPIKE-segm, taken from the publicly accessible SPIKE dataset, and an optimal instance segmentation approach named as WheatSpikeNet for segmenting and counting wheat spikes from field imagery. The proposed method is based on the well-known Cascade Mask RCNN architecture with model enhancements and hyperparameter tuning to provide state-of-the-art detection and segmentation performance. A comprehensive ablation analysis incorporating many architectural components of the model was performed to determine the most efficient version. In addition, the model's hyperparameters were fine-tuned by conducting several empirical tests. ResNet50 with Deformable Convolution Network (DCN) as the backbone architecture for feature extraction, Generic RoI Extractor (GRoIE) for RoI pooling, and Side Aware Boundary Localization (SABL) for wheat spike localization comprises the final instance segmentation model. With bbox and mask mean average precision (mAP) scores of 0.9303 and 0.9416, respectively, on the test set, the proposed model achieved superior performance on the challenging SPIKE datasets. Furthermore, in comparison with other existing state-of-the-art methods, the proposed model achieved up to a 0.41% improvement of mAP in spike detection and a significant improvement of 3.46% of mAP in the segmentation tasks that will lead us to an appropriate yield estimation from wheat plants.

Biosensor Based on Graphene Directly Grown by MW-PECVD for Detection of COVID-19 Spike (S) Protein and Its Entry Receptor ACE2.

Biosensors based on graphene field-effect transistors (G-FET) for detecting COVID-19 spike S protein and its receptor ACE2 were reported. The graphene, directly synthesized on SiO2/Si substrate by microwave plasma-enhanced chemical vapor deposition (MW-PECVD), was used for FET biosensor fabrication. The commercial graphene, CVD-grown on a copper substrate and subsequently transferred onto a glass substrate, was applied for comparison purposes. The graphene structure and surface morphology were studied by Raman scattering spectroscopy and atomic force microscope. Graphene surfaces were functionalized by an aromatic molecule PBASE (1-pyrenebutanoic acid succinimidyl ester), and subsequent immobilization of the receptor angiotensin-converting enzyme 2 (ACE2) was performed. A microfluidic system was developed, and transfer curves of liquid-gated FET were measured after each graphene surface modification procedure to investigate ACE2 immobilization by varying its concentration and subsequent spike S protein detection. The directly synthesized graphene FET sensitivity to the receptor ACE2, evaluated in terms of the Dirac voltage shift, exceeded the sensitivity of the transferred commercial graphene-based FET. The concentration of the spike S protein was detected in the range of 10 ag/mL up to 10 μg/mL by using a developed microfluidic system and measuring the transfer characteristics of the liquid-gated G-FETs. It was found that the shift of the Dirac voltage depends on the spike S concentration and was 27 mV with saturation at 10 pg/mL for directly synthesized G-FET biosensor, while for transferred G-FET, the maximal shift of 70 mV was obtained at 10 μg/mL with a tendency of saturation at 10 ng/mL. The detection limit as low as 10 ag/mL was achieved for both G-FETs. The sensitivity of the biosensors at spike S concentration of 10 pg/mL measured as relative current change at a constant gate voltage corresponding to the highest transconductance of the G-FETs was found at 5.6% and 8.8% for directly synthesized and transferred graphene biosensors, respectively. Thus, MW-PECVD-synthesized graphene-based biosensor demonstrating high sensitivity and low detection limit has excellent potential for applications in COVID-19 diagnostics.

Microbial Fuel Cell Biosensor with Capillary Carbon Source Delivery for Real-Time Toxicity Detection.

A microbial fuel cell (MFC) biosensor with an anode as a sensing element is often unreliable at low or significantly fluctuating organic matter concentrations. To remove this limitation, this work demonstrates capillary action-aided carbon source delivery to an anode-sensing MFC biosensor for use in carbon-depleted environments, e.g., potable water. First, different carbon source delivery configurations using several thread types, silk, nylon, cotton, and polyester, are evaluated. Silk thread was determined to be the most suitable material for passive delivery of a 40 g L-1 acetate solution. This carbon source delivery system was then incorporated into the design of an MFC biosensor for real-time detection of toxicity spikes in tap water, providing an organic matter concentration of 56 ± 15 mg L-1. The biosensor was subsequently able to detect spikes of toxicants such as chlorine, formaldehyde, mercury, and cyanobacterial microcystins. The 16S sequencing results demonstrated the proliferation of Desulfatirhabdium (10.7% of the total population), Pelobacter (10.3%), and Geobacter (10.2%) genera. Overall, this work shows that the proposed approach can be used to achieve real-time toxicant detection by MFC biosensors in carbon-depleted environments.

Automated Seizure Detection in Neonatal EEG using Signal Processing Algorithms

The crucial challenge of identifying seizures in newborn electroencephalogram (EEG) data is the subject of this research. (ElectroEncephaloGram) EEG analysis has traditionally been labor-intensive and subjective due to the heavy reliance on human inspection by skilled electrophysiologists and neurologists. The authors suggest a novel online automatic detection approach to get over these restrictions. This approach intends to open the door for effective and reliable early seizure detection, delivering a potential development in neonatal care and neurological research. It does this by utilizing the capabilities of signal processing and brain-inspired modeling’s suggested machine-learning method makes effective use of real-time EEG signal processing methods. Its processing of incoming EEG data streams makes it appropriate for neonates to be closely tracked in critical care situations. Automation of the detection method minimizes the need for manual inspection by highly specialized experts, allowing for more rapid responses and possibly increasing seizure management's overall results. Using metal electrodes, the electroencephalogram (EEG) analyses the electrical activity of the brain. European Data Format (EDF) BROWSER and EEG STUDIO are used to analyses EEG signals. Root Mean Square (RMS) and signal samples are performed by EDF BROWSER, as well as mean, frequency, and frequency from filtered output using a band-pass filter. The standard deviation and average frequency are examined by EEG STUDIO. Spike detection, frequency domain analysis, and nonlinear techniques are used in seizure prediction. EOG, EKG, and EMG artifacts produced by the eyes, the heart, and the muscles, respectively may be present in EEG readings.

A Fast and Effective Spike Sorting Method Based on Multi-Frequency Composite Waveform Shapes.

Accurate spike sorting to the appropriate neuron is crucial for neural activity analysis. To improve spike sorting performance, it is essential to fully leverage each processing step, including filtering, spike detection, feature extraction, and clustering. However, compared to the latter two steps that were widely studied and optimized, the filtering process was largely neglected. In this study, we proposed a fast and effective spike sorting method (MultiFq) based on multi-frequency composite waveform shapes acquired through an optimized filtering process. When combined with the classical PCA-Km spiking sorting algorithm, our proposed MultiFq significantly improved its sorting performance and achieved as high performance as the complex Wave-clus did in both the simulated and in vivo datasets. But, the combined method was about 10 times faster than Wave-clus (0.16 s vs. 2.06 s in simulated datasets; 0.46 s vs. 2.03 s in in vivo datasets). Furthermore, we demonstrated the compatibility of our MultiFq by combining it with other sorting algorithms, which consistently resulted in significant improvement in sorting accuracy with the maximum improvement at 35.04%. The above results demonstrated that our proposed method could significantly improve the sorting performance with low computation cost and good compatibility by leveraging the multi-frequency composite waveform shapes.

Calibration-Free and Hardware-Efficient Neural Spike Detection for Brain Machine Interfaces.

Recent translational efforts in brain-machine interfaces (BMI) are demonstrating the potential to help people with neurological disorders. The current trend in BMI technology is to increase the number of recording channels to the thousands, resulting in the generation of vast amounts of raw data. This in turn places high bandwidth requirements for data transmission, which increases power consumption and thermal dissipation of implanted systems. On-implant compression and/or feature extraction are therefore becoming essential to limiting this increase in bandwidth, but add further power constraints - the power required for data reduction must remain less than the power saved through bandwidth reduction. Spike detection is a common feature extraction technique used for intracortical BMIs. In this article, we develop a novel firing-rate-based spike detection algorithm that requires no external training and is hardware efficient and therefore ideally suited for real-time applications. Key performance and implementation metrics such as detection accuracy, adaptability in chronic deployment, power consumption, area utilization, and channel scalability are benchmarked against existing methods using various datasets. The algorithm is first validated using a reconfigurable hardware (FPGA) platform and then ported to a digital ASIC implementation in both 65 nm and 0.18 μm CMOS technologies. The 128-channel ASIC design implemented in a 65 nm CMOS technology occupies 0.096 mm2 silicon area and consumes 4.86 μW from a 1.2 V power supply. The adaptive algorithm achieves a 96% spike detection accuracy on a commonly used synthetic dataset, without the need for any prior training.

Data Compression Versus Signal Fidelity Tradeoff in Wired-OR Analog-to-Digital Compressive Arrays for Neural Recording.

Future high-density and high channel count neural interfaces that enable simultaneous recording of tens of thousands of neurons will provide a gateway to study, restore and augment neural functions. However, building such technology within the bit-rate limit and power budget of a fully implantable device is challenging. The wired-OR compressive readout architecture addresses the data deluge challenge of a high channel count neural interface using lossy compression at the analog-to-digital interface. In this article, we assess the suitability of wired-OR for several steps that are important for neuroengineering, including spike detection, spike assignment and waveform estimation. For various wiring configurations of wired-OR and assumptions about the quality of the underlying signal, we characterize the trade-off between compression ratio and task-specific signal fidelity metrics. Using data from 18 large-scale microelectrode array recordings in macaque retina ex vivo, we find that for an event SNR of 7-10, wired-OR correctly detects and assigns at least 80% of the spikes with at least 50× compression. The wired-OR approach also robustly encodes action potential waveform information, enabling downstream processing such as cell-type classification. Finally, we show that by applying an LZ77-based lossless compressor (gzip) to the output of the wired-OR architecture, 1000× compression can be achieved over the baseline recordings.

A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes

This paper proposes a new hybrid model to forecast electricity market prices up to four days ahead. The components of the proposed model are combined in two dimensions. First, on the “vertical” dimension, long short-term memory (LSTM) neural networks and extreme gradient boosting (XGBoost) models are stacked up to produce supplementary price forecasts. The final forecasts are then picked depending on how the predictions compare to a price spike threshold. On the “horizontal” dimension, five models are designed to extend the forecasting horizon to four days. This is an important requirement to make forecasts useful for market participants who trade energy and ancillary services multiple days ahead. The horizontally cascaded models take advantage of the availability of specific public data for each forecasting horizon. To enhance the forecasting capability of the model in dealing with price spikes, we deploy a previously unexplored input in the proposed methodology. That is, to use the recent variations in the output power of thermal units as an indicator of unplanned outages or shift in the supply stack. The proposed method is tested using data from Alberta’s electricity market, which is known for its volatility and price spikes. An economic application of the developed forecasting model is also carried out to demonstrate how several market players in the Alberta electricity market can benefit from the proposed multi-day ahead price forecasting model. The numerical results demonstrate that the proposed methodology is effective in enhancing forecasting accuracy and price spike detection.

An improved YOLOv5-based algorithm for small wheat spikes detection

An improved YOLOv5-based algorithm for small wheat spikes detection

Magnetoencephalography-based approaches to epilepsy classification.

Epilepsy is a chronic central nervous system disorder characterized by recurrent seizures. Not only does epilepsy severely affect the daily life of the patient, but the risk of premature death in patients with epilepsy is three times higher than that of the normal population. Magnetoencephalography (MEG) is a non-invasive, high temporal and spatial resolution electrophysiological data that provides a valid basis for epilepsy diagnosis, and used in clinical practice to locate epileptic foci in patients with epilepsy. It has been shown that MEG helps to identify MRI-negative epilepsy, contributes to clinical decision-making in recurrent seizures after previous epilepsy surgery, that interictal MEG can provide additional localization information than scalp EEG, and complete excision of the stimulation area defined by the MEG has prognostic significance for postoperative seizure control. However, due to the complexity of the MEG signal, it is often difficult to identify subtle but critical changes in MEG through visual inspection, opening up an important area of research for biomedical engineers to investigate and implement intelligent algorithms for epilepsy recognition. At the same time, the use of manual markers requires significant time and labor costs, necessitating the development and use of computer-aided diagnosis (CAD) systems that use classifiers to automatically identify abnormal activity. In this review, we discuss in detail the results of applying various different feature extraction methods on MEG signals with different classifiers for epilepsy detection, subtype determination, and laterality classification. Finally, we also briefly look at the prospects of using MEG for epilepsy-assisted localization (spike detection, high-frequency oscillation detection) due to the unique advantages of MEG for functional area localization in epilepsy, and discuss the limitation of current research status and suggestions for future research. Overall, it is hoped that our review will facilitate the reader to quickly gain a general understanding of the problem of MEG-based epilepsy classification and provide ideas and directions for subsequent research.

Photoelectrochemical biosensing platform for the SARS‐CoV‐2 spike and nucleocapsid proteins

AbstractThe COVID‐19 pandemic is still a continuing worldwide challenge for public health systems. Early and ultrasensitive identification of the infection is essential for preventing the spread of COVID‐19 by pre‐symptomatic or asymptomatic individuals, particularly in the community and in‐home settings. This work presents a versatile photoelectrochemical (PEC) immunosensor for SARS‐CoV‐2 detection based on a composite material formed by bismuth vanadate (BiVO4) and strontium titanate (SrTiO3). The PEC platform was denoted as BiVO4/SrTiO3/FTO, and it can be tuned for the detection of either Spike (S) or Nucleocapsid (N) protein by simply altering the antibody immobilized on the platform's surface. Chemical, morphological, and electrochemical characterizations were performed by X‐Ray Diffraction, Scanning Electron microscopy, Energy‐dispersive X‐ray spectroscopy, Electrochemical Impedance Spectroscopy, and Amperometry. With a simple sensing architecture of the PEC platform, it was possible to achieve a linear response range of 0.1 pg mL−1 to 1000 ng mL−1 for S protein and 0.01 pg mL−1 to 1000 ng mL−1 for N protein. The PEC immunosensors presented recovery values for the two SARS‐CoV‐2 proteins in artificial saliva samples between 97 % and 107.20 % suggesting a good accuracy for the proposed immunosensors.