Surface electromyography (sEMG) records muscle activities from the surface of muscles, which offers a wealth of information concerning muscle activation patterns in both research and clinical settings. A key principle underlying sEMG analyses is the decomposition of the signal into a number of motor unit action potentials (MUAPs) that capture most of the relevant features embedded in a low-dimensional space. Toward this, the principal component analysis (PCA) has extensively been sought after, whereby the original sEMG data are translated into low-dimensional MUAP components with a reduced level of redundancy. The objective of this paper is to disseminate the role of PCA in conjunction with the quantitative sEMG analyses. Following the preliminaries on the sEMG methodology and a statement of PCA algorithm, an exhaustive collection of PCA applications related to sEMG data is in order. Alongside the technical challenges associated with the PCA-based sEMG processing, the envisaged research trend is also discussed.INDEX TERMS Surface electromyography (sEMG), artificial neural network (ANN), principal component analysis (PCA), motor unit action potential (MUAP), flexions, self-organizing feature map (SOFM), support vector regression (SVR), myoelectric signal.

Finding an appropriate technique to detect an islanding issue is one of the major challenges associated with the design of a resilient grid-linked photovoltaic-based distributed power generation (PV-DPG) system. In general, the technique used for islanding detection must be able to sense the disruptions from the electric grid and quickly disconnect PV-DPG from the grid. The quick disconnection of PV-DPG mostly avoids power quality problems, damage to power assets, voltage stability issues, and frequency instability. In this paper, a new islanding detection technique that is based on tunable Q-factor wavelet transform (TQWT) and an artificial neural network (ANN) is proposed for PV-DPG. The proposed approach consists of two steps: in the first step, the vital detection parameters are computed by performing simulations considering all possible switching transients, islanding events, and faults from the grid side. Then, the decomposition of obtained signals is done using TQWT on different levels. Using the obtained coefficients, at each level, features such as range, minimum, mean, standard deviation, maximum, energy, and log energy entropy are computed. The optimal feature set was selected as the input for the second step. The classification of the non-islanding and islanding states for PV-DPG is made using the ANN classifier in the second step, which achieved an accuracy of 98%. The results representing the efficiency of the proposed approach in noisy and non-noisy environments are also explained. Overall, it is understood that the proposed islanding detection technique would provide suitable insights to detect an islanding issue.

This article presents an online accessible electroencephalogram (EEG) database, where the EEG recordings comprise abnormal patterns such as spikes, poly spikes, slow waves, and sharp waves to help diagnose related disorders. The data, as of now, are a collection of EEGs from a diagnostic center in Coimbatore, Tamil Nadu, India, and the data samples pertain to an age-group ranging from 1 to 107 years. Eventually, the EEG data concerning other disorders as well as those from other institutions will be included. The present database provides information under the following categories: major classification of the disorder, patient's record, digitized EEG, and specific diagnosis; in addition, a search facility is incorporated into the database. The mode of access by the domain experts, application developers, and researchers, along with a few classical applications are explained in this article. With the advance of clinical neuroscience, this database will be helpful in developing software for applications such as diagnosis and treatment.

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This article presents the design and validation of an accurate automatic diagnostic system to classify intramuscular EMG (iEMG) signals into healthy, myopathy, or neuropathy categories to aid the diagnosis of neuromuscular diseases. Methods: First, an iEMG signal is decimated to produce a set of "disjoint" downsampled signals, which are decomposed by the lifting wavelet transform (LWT). The Higuchi's fractal dimensions (FDs) of LWT coefficients in the subbands are computed. The FDs of LWT subband coefficients are fused with one-dimensional local binary pattern derived from each downsampled signal. Next, a multilayer perceptron neural network (MLPNN) determines the class labels of downsampled signals. Finally, the sequence of class labels is fed to the Boyer-Moore majority vote (BMMV) algorithm, which assigns a class to every iEMG signal. Results: The MLPNN-BMMV classifier was experimented with 250 iEMG signals belonging to three categories. The performance of the classifier was validated in comparison with state-of-the-art approaches. The MLPNN-BMMV has resulted in impressive performance measures (%) using a 10-fold cross-validation-accuracy = 99.87 ± 0.25, sensitivity (normal) = 99.97 ± 0.13, sensitivity (myopathy) = 99.68 ± 0.95, sensitivity (neuropathy) = 99.76 ± 0.66, specificity (normal) = 99.72 ± 0.61, specificity (myopathy) = 99.98 ± 0.10, and specificity (neuropathy) = 99.96 ± 0.14-surpassing the existing approaches. Conclusions: A future research direction is to validate the classifier performance with diverse iEMG datasets, which would lead to the design of an affordable real-time expert system for neuromuscular disorder diagnosis. Index Terms-Fractal dimension, intramuscular electromyography, lifting wavelet transform, local binary pattern, majority vote, multilayer perceptron neural network, neuromuscular disorders. Impact Statement-An iEMG classifier is designed and validated with iEMG signals from healthy, myopathy, and neuropathy subjects. The classifier's outstanding performance is a step toward real-time diagnostic system for neuromuscular diseases.