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Steady-state visually evoked potential is one of the active explorations in the brain-computer interface research. Electroencephalogram based brain computer interface studies have been widely applied to perceive solutions for real-world problems in the healthcare domain. The classification of externally bestowed visual stimuli of different frequencies on a human was experimented to identify the need of paralytic people. Although many classifiers are at the fingertip of machine learning technology, recent research has proven that ensemble learning is more efficacious than individual classifiers. Despite its efficiency, ensemble learning technology exhibits certain drawbacks like taking more time on selecting the optimal classifier subset. This research article utilizes the Harris Hawk Optimization algorithm to select the best classifier subset from the given set of classifiers. The objective of the research is to develop an efficient multi-classifier model for electroencephalogram signal classification. The proposed model utilizes the Boruta Feature Selection algorithm to select the prominent features for classification. Thus selected prominent features are fed into the multi-classifier subset which has been generated by the Harris Hawk Optimization algorithm. The results of the multi-classifier ensemble model are aggregated using Stacking, Bagging, Boosting, and Voting. The proposed model is evaluated against the acquired dataset and produces a promising accuracy of 96.1%, 98.7%, 91.91%, and 99.01% with the ensemble techniques respectively. The proposed model is also validated with other performance metrics such as sensitivity, specificity, and F1-Score. The experimental results show that the proposed model proves its supremacy in segregating the multi-class classification problem with high accuracy.

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Classification of electroencephalography (EEG) signals associated with Steady-state visually evoked potential (SSVEP) is prominent because of its potential in restoring the communication and controlling capability of paralytic people. However, SSVEP signals classification is a challenging task for researchers because of its low signal-to-noise ratio, non-stationary and high dimensional properties. A proficient technique has to be evolved to classify the SSVEP-based EEG data. In recent times, convolutional neural network (CNN) has reached a quantum leap in EEG signal classification. Therefore, the proposed system employs CNN to classify the SSVEP-based EEG signals. Though CNN has proved its proficiency in handling EEG signal classification problems, the calibration of hyperparameters is required to enhance the performance of the model. The calibration of a hyperparameter is a time-consuming task, hence proposed an automated hyperparameter optimization technique using the Red Fox Optimization Algorithm (RFO). The effectiveness of the algorithm is evaluated by comparing it with the performance of Harris Hawk Optimization (HHO), Flower Pollination Algorithm (FPA), Grey Wolf Optimization Algorithm (GWO) and Whale Optimization Algorithm (WOA) based hyperparameter optimized CNN applied to the SSVEP based EEG signals multiclass dataset. The experimental results infer that the proposed algorithm can achieve a testing accuracy of 88.91% which is higher than other comparative algorithms like HHO, FPA, GWO and WOA. The above-mentioned values clearly show that the proposed algorithm achieved competitive performance when compared to the other reported algorithm.

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Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children's Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.

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The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh-Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.

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Brain-computer interfaces are sophisticated signal processing systems, which directly operate on neuronal signals to identify specific human intents. These systems can be applied to overcome certain disabilities or to enhance the natural capabilities of human beings. The visual P300 mind-speller is a prominent one among them, which has opened up tremendous possibilities in movement and communication applications. Today, there exist many state-of-the-art visual P300 mind-speller implementations in the literature as a result of numerous researches in this domain over the past 2 decades. Each of these systems can be evaluated in terms of performance metrics like classification accuracy, information transfer rate, and processing time. Various classification techniques associated with these systems, which include but are not limited to discriminant analysis, support vector machine, neural network, distance-based and ensemble of classifiers, have major roles in determining the overall system performances. The significance of a proper review on the recent developments in visual P300 mind-spellers with proper emphasis on their classification algorithms is the key insight for this work. This article is organized with a brief introduction to P300, concepts of visual P300 mind-spellers, the survey of literature with special focus on classification algorithms, followed by the discussion of various challenges and future directions.

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Goal: 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 = [Formula: see text], sensitivity (normal) = [Formula: see text], sensitivity (myopathy) = [Formula: see text], sensitivity (neuropathy) = [Formula: see text], specificity (normal) = [Formula: see text], specificity (myopathy) = [Formula: see text], and specificity (neuropathy) = [Formula: see text]-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.

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EEG records the spontaneous electrical activity of the brain using multiple electrodes placed on the scalp, and it provides a wealth of information related to the functions of brain. Nevertheless, the signals from the electrodes cannot be directly applied to a diagnostic tool like brain mapping as they undergo a "mixing" process because of the volume conduction effect in the scalp. A pervasive problem in neuroscience is determining which regions of the brain are active, given voltage measurements at the scalp. Because of which, there has been a surge of interest among the biosignal processing community to investigate the process of mixing and unmixing to identify the underlying active sources. According to the assumptions of independent component analysis (ICA) algorithms, the resultant mixture obtained from the scalp can be closely approximated by a linear combination of the "actual" EEG signals emanating from the underlying sources of electrical activity in the brain. As a consequence, using these well-known ICA techniques in preprocessing of the EEG signals prior to clinical applications could result in development of diagnostic tool like quantitative EEG which in turn can assist the neurologists to gain noninvasive access to patient-specific cortical activity, which helps in treating neuropathologies like seizure disorders. The popular and proven ICA schemes mentioned in various literature and applications were selected (which includes Infomax, JADE, and SOBI) and applied on generalized seizure disorder samples using EEGLAB toolbox in MATLAB environment to see their usefulness in source separations; and they were validated by the expert neurologist for clinical relevance in terms of pathologies on brain functionalities. The performance of Infomax method was found to be superior when compared with other ICA schemes applied on EEG and it has been established based on the validations carried by expert neurologist for generalized seizure and its clinical correlation. The results are encouraging for furthering the studies in the direction of developing useful brain mapping tools using ICA methods.

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Independent component analysis (ICA) aims at separating a multivariate signal into independent nongaussian signals by optimizing a contrast function with no knowledge on the mixing mechanism. Despite the availability of a constellation of contrast functions, a Hartley-entropy-based ICA contrast endowed with the discriminacy property makes it an appealing choice as it guarantees the absence of mixing local optima. Fueled by an outstanding source separation performance of this contrast function in practical instances, a succession of optimization techniques has recently been adopted to solve the ICA problem. Nevertheless, the nondifferentiability of the considered contrast restricts the choice of the optimizer to the class of derivative-free methods. On the contrary, this letter concerns a Riemannian quasi-Newton scheme involving an explicit expression for the gradient to optimize the contrast function that is differentiable almost everywhere. Furthermore, the inexact line search insisting on the weak Wolfe condition and a terminating criterion befitting the partly smooth function optimization have been generalized to manifold settings, leaving the previous results intact. The investigations with diversified images and the electroencephalographic (EEG) data acquired from 45 focal epileptic subjects demonstrate the efficacy of our approach in terms of computational savings and reliable EEG source localization, respectively. Additional experimental results are available in the online supplement.

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