Characterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Technological developments have enabled the collection of hundreds of neural recordings; this calls for new tools capable of performing feature extraction efficiently. To address the urgent need for a powerful and accessible tool, we developed ElecFeX, an open-source MATLAB-based toolbox that (1) has an intuitive graphical user interface, (2) provides customizable measurements for a wide range of electrophysiological features, (3) processes large-size datasets effortlessly via batch analysis, and (4) yields formatted output for further analysis. We implemented ElecFeX on a diverse set of neural recordings; demonstrated its functionality, versatility, and efficiency in capturing electrical features; and established its significance in distinguishing neuronal subgroups across brain regions and species. ElecFeX is thus presented as a user-friendly toolbox to benefit the neuroscience community by minimizing the time required for extracting features from their electrophysiological datasets.

UNASSIGNED: Children with benign childhood epilepsy with centro-temporal spikes (BECT) have spikes, sharps, and composite waves on their electroencephalogram (EEG). It is necessary to detect spikes to diagnose BECT clinically. The template matching method can identify spikes effectively. However, due to the individual specificity, finding representative templates to detect spikes in actual applications is often challenging.
UNASSIGNED: This paper proposes a spike detection method using functional brain networks based on phase locking value (FBN-PLV) and deep learning.
UNASSIGNED: To obtain high detection effect, this method uses a specific template matching method and the \'peak-to-peak\' phenomenon of montages to obtain a set of candidate spikes. With the set of candidate spikes, functional brain networks (FBN) are constructed based on phase locking value (PLV) to extract the features of the network structure during spike discharge with phase synchronization. Finally, the time domain features of the candidate spikes and the structural features of the FBN-PLV are input into the artificial neural network (ANN) to identify the spikes.
UNASSIGNED: Based on FBN-PLV and ANN, the EEG data sets of four BECT cases from the Children\'s Hospital, Zhejiang University School of Medicine are tested with the AC of 97.6%, SE of 98.3%, and SP 96.8%.

The application of computer vision in agriculture has already contributed immensely to restructuring the existing field practices starting from the sowing to the harvesting. Among the different plant parts, the economic part, the yield, has the highest importance and becomes the ultimate goal for the farming community. It depends on many genetic and environmental factors, so this curiosity about knowing the yield brought several precise pre-harvest prediction methods using different ways. Out of those techniques, non-invasive yield prediction techniques using computer vision have been proved to be the most efficient and trusted platform. This study developed a novel methodology, called SlypNet, using advanced deep learning networks, i.e., Mask R-CNN and U-Net, which can extract various plant morphological features like spike and spikelet from the visual image of the wheat plant and provide a high-throughput yield estimate with great precision. Mask R-CNN outperformed previous networks in spike detection by its precise detection performance with a mean average precision (mAP) of 97.57%, a F1 score of 0.67, and an MCC of 0.91 by overcoming several natural field constraints like overlapping and background interference, variable resolution, and high bushiness of plants. The spikelet detection module\'s accuracy and consistency were tested with about 99% validation accuracy of the model and the least error, i.e., a mean square error of 1.3 from a set of typical and complex views of wheat spikes. Spikelet yield cumulatively showed the probable production capability of each plant. Our method presents an integrated deep learning platform of spikelet-based yield prediction comprising spike and spikelet detection, leading to higher precision over the existing methods.

Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top of the grain plants growing in field conditions. However, accurate and automated analysis of all fully and partially visible spikes in greenhouse images renders a more challenging task, which was rarely addressed in the past. A particular difficulty for image analysis is represented by leaf-covered, occluded but also matured spikes of bushy crop cultivars that can hardly be differentiated from the remaining plant biomass. To address the challenge of automated analysis of arbitrary spike phenotypes in different grain crops and optical setups, here, we performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images, including five deep and one shallow neural network. Our experimental results demonstrate that advanced deep learning methods show superior performance, achieving over 90% accuracy by detection and segmentation of spikes in wheat, barley and rye images. However, spike detection in new crop phenotypes can be performed more accurately than segmentation. Furthermore, the detection and segmentation of matured, partially visible and occluded spikes, for which phenotypes substantially deviate from the training set of regular spikes, still represent a challenge to neural network models trained on a limited set of a few hundreds of manually labeled ground truth images. Limitations and further potential improvements of the presented algorithmic frameworks for spike image analysis are discussed. Besides theoretical and experimental investigations, we provide a GUI-based tool (SpikeApp), which shows the application of pre-trained neural networks to fully automate spike detection, segmentation and phenotyping in images of greenhouse-grown plants.

In recent years, nano-impact electrochemistry (NIE) has attracted widespread attention as a new electroanalytical approach for the analysis and characterization of single nanoparticles in solution. The accurate analysis of the large volume of the experimental data is of great significance in improving the reliability of this method. Unfortunately, the commonly used data analysis approaches, mainly based on manual processing, are often time-consuming and subjective. Herein, we propose a spike detection algorithm for automatically processing the data from the direct oxidation of sliver nanoparticles (AgNPs) in NIE experiments, including baseline extraction, spike identification and spike area integration. The resulting size distribution of AgNPs is found to agree very well with that from transmission electron microscopy (TEM), showing that the current algorithm is promising for automated analysis of NIE data with high efficiency and accuracy.

Intracranial pressure (ICP) signals are often contaminated by artefacts and segments of missing values. Some of these artefacts can be observed as very high and short spikes with a physiologically impossible high slope. The presence of these spikes reduces the accuracy of pattern recognition techniques. Thus, we propose a modified empirical mode decomposition (EMD) method for spike removal in raw ICP signals. The EMD breaks down the signal into 16 intrinsic mode functions (IMFs), combines the first 4 to localize spikes using adaptive thresholding, and then either removes or imputes the identified ICP spikes.

The progress in microtechnology has enabled an exponential trend in the number of neurons that can be simultaneously recorded. The data bandwidth requirement is however increasing with channel count. The vast majority of experimental work involving electrophysiology stores the raw data and then processes this offline; to detect the underlying spike events. Emerging applications however require new methods for local, real-time processing.
We have developed an adaptive, low complexity spike detection algorithm that combines three novel components for: (1) removing the local field potentials; (2) enhancing the signal-to-noise ratio; and (3) computing an adaptive threshold. The proposed algorithm has been optimised for hardware implementation (i.e. minimising computations, translating to a fixed-point implementation), and demonstrated on low-power embedded targets.
The algorithm has been validated on both synthetic datasets and real recordings yielding a detection sensitivity of up to 90%. The initial hardware implementation using an off-the-shelf embedded platform demonstrated a memory requirement of less than 0.1 kb ROM and 3 kb program flash, consuming an average power of 130 μW.
The method presented has the advantages over other approaches, that it allows spike events to be robustly detected in real-time from neural activity in a completely autonomous way, without the need for any calibration, and can be implemented with low hardware resources.
The proposed method can detect spikes effectively and adaptively. It alleviates the need for re-calibration, which is critical towards achieving a viable BMI, and more so with future \'high bandwidth\' systems\' targeting 1000s of channels.

This study explores the generation of ultrafine particle emissions, measured in particle number (PN), based on a portable emissions measurement system (PEMS) in the City of Toronto between October and December 2019. Two driving routes were designed to include busy arterial roads and highways. All measurements were conducted between 10 a.m. and 4 p.m. Altogether, emissions from 31 drives were collected, leading to approximately 200,000 s of data. A spike detection algorithm was employed to isolate PN spikes in time series data. A sensitivity analysis was also conducted to identify the most optimum method for spike detection. The results indicate that the average emission rate during a PN spike is approximately 8 times the emission rate along the rest of the drive. In each test trip, about 25% of the duration was attributed to spike events, contributing 75% of total PN emissions. A Pearson correlation of 0.45 was estimated between the number of PN spikes and the number of sharp accelerations (above 8.5 km/h/s). The Pearson correlation between the occurrence of high engine torque (above 65.0 Nm) and the number of PN spikes was estimated at 0.80. The number of PN spikes was highest on arterial roads where the vehicle speed was relatively low, but with high variability, and including a high number of sharp accelerations. This pattern of UFP emissions leads to high UFP concentrations along arterial roads in the inner city core.

Visual evaluation of electroencephalogram (EEG) for Interictal Epileptiform Discharges (IEDs) as distinctive biomarkers of epilepsy has various limitations, including time-consuming reviews, steep learning curves, interobserver variability, and the need for specialized experts. The development of an automated IED detector is necessary to provide a faster and reliable diagnosis of epilepsy. In this paper, we propose an automated IED detector based on Convolutional Neural Networks (CNNs). We have evaluated the proposed IED detector on a sizable database of 554 scalp EEG recordings (84 epileptic patients and 461 nonepileptic subjects) recorded at Massachusetts General Hospital (MGH), Boston. The proposed CNN IED detector has achieved superior performance in comparison with conventional methods with a mean cross-validation area under the precision-recall curve (AUPRC) of 0.838[Formula: see text]±[Formula: see text]0.040 and false detection rate of 0.2[Formula: see text]±[Formula: see text]0.11 per minute for a sensitivity of 80%. We demonstrated the proposed system to be noninferior to 30 neurologists on a dataset from the Medical University of South Carolina (MUSC). Further, we clinically validated the system at National University Hospital (NUH), Singapore, with an agreement accuracy of 81.41% with a clinical expert. Moreover, the proposed system can be applied to EEG recordings with any arbitrary number of channels.

Spike is one of the crop yield organs in wheat plants. Determination of the phenological stages, including heading time point (HTP), and area of spike from non-invasive phenotyping images provides the necessary information for the inference of growth-related traits. The algorithm previously developed by Qiongyan et al. for spike detection in 2-D images turns out to be less accurate when applied to the European cultivars that produce many more leaves. Therefore, we here present an improved and extended method where (i) wavelet amplitude is used as an input to the Laws texture energy-based neural network instead of original grayscale images and (ii) non-spike structures (e.g., leaves) are subsequently suppressed by combining the result of the neural network prediction with a Frangi-filtered image. Using this two-step approach, a 98.6% overall accuracy of neural network segmentation based on direct comparison with ground-truth data could be achieved. Moreover, the comparative error rate in spike HTP detection and growth correlation among the ground truth, the algorithm developed by Qiongyan et al., and the proposed algorithm are discussed in this paper. The proposed algorithm was also capable of significantly reducing the error rate of the HTP detection by 75% and improving the accuracy of spike area estimation by 50% in comparison with the Qionagyan et al. method. With these algorithmic improvements, HTP detection on a diverse set of 369 plants was performed in a high-throughput manner. This analysis demonstrated that the HTP of 104 plants (comprises of 57 genotypes) with lower biomass and tillering range (e.g., earlier-heading types) were correctly determined. However, fine-tuning or extension of the developed method is required for high biomass plants where spike emerges within green bushes. In conclusion, our proposed method allows significantly more reliable results for HTP detection and spike growth analysis to be achieved in application to European cultivars with earlier-heading types.