Human scalp EEG processing: Various soft computing approaches

Majumdar, Kaushik

doi:10.1016/j.asoc.2011.07.004

Cited by 40 publications

(19 citation statements)

References 217 publications

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“…In a multi-class scenario having M classes, p e = 1/M and the classification accuracy (p 0 ) and Kappa coefficient are related as shown in Eq. (30). Hence we can directly compute the Kappa coefficient from the classification accuracy p 0 as shown in Eq.…”

Section: Datasets Descriptionmentioning

confidence: 99%

“…A technique of spatial filtering, Common Spatial Patterns (CSP) [24], has conventionally been used on EEG data for BCI systems [30]. It is essentially a decomposition technique for separating a multivariate signal into principal components, and selecting a subset that retains at least a specified fraction of the total information content.…”

Section: Challenges In Motor-imagery Bcismentioning

confidence: 99%

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High performance EEG signal classification using classifiability and the Twin SVM

Soman

Jayadeva

2015

Applied Soft Computing

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Section: Datasets Descriptionmentioning

confidence: 99%

Section: Challenges In Motor-imagery Bcismentioning

confidence: 99%

High performance EEG signal classification using classifiability and the Twin SVM

Soman

Jayadeva

2015

Applied Soft Computing

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“…Conceding to the equation 9, the error can be estimated by determining the difference between desired and real output. Here, the MSE is taken as a function [31][32] with parameters as center (c), spread (σ) and weight (w).…”

Section: Learning Of Rbfnnmentioning

confidence: 99%

EEG signal classification using PSO trained RBF neural network for epilepsy identification

Satapathy

Dehuri

Jagadev

2017

Informatics in Medicine Unlocked

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The electroencephalogram (EEG) is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT). To classify the EEG signal, we used a radial basis function neural network (RBFNN). As shown herein, the network can be trained to optimize the mean square error (MSE) by using a modified particle swarm optimization (PSO) algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques.

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“…McSharry et al [8] discussed and enumerated the nonlinear methods and its relevance to predict epilepsy by considering EEG samples as time series. Majumdar [15] reviews various soft computing approaches of EEG signals which emphasize more on pattern recognition techniques. The paper [15] mainly focuses on dimensionality reduction, SNR problems, linear and soft computing techniques for EEG signal processing.…”

Section: Related Workmentioning

confidence: 99%

“…Majumdar [15] reviews various soft computing approaches of EEG signals which emphasize more on pattern recognition techniques. The paper [15] mainly focuses on dimensionality reduction, SNR problems, linear and soft computing techniques for EEG signal processing. Majumdar concludes that the neural network and Bayesian approaches are two popular choices even though linear statistical discriminants are easier to implement.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of wavelet transforms and morphological operator-based classification of epilepsy risk levels

Rajaguru¹,

Vijayakumar²

2014

EURASIP J. Adv. Signal Process.

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The objective of this paper is to compare the performance of singular value decomposition (SVD), expectation maximization (EM), and modified expectation maximization (MEM) as the postclassifiers for classifications of the epilepsy risk levels obtained from extracted features through wavelet transforms and morphological filters from electroencephalogram (EEG) signals. The code converter acts as a level one classifier. The seven features such as energy, variance, positive and negative peaks, spike and sharp waves, events, average duration, and covariance are extracted from EEG signals. Out of which four parameters like positive and negative peaksand spike and sharp waves, events and average duration are extracted using Haar, dB2, dB4, and Sym 8 wavelet transforms with hard and soft thresholding methods. The above said four features are also extracted through morphological filters. Then, the performance of the code converter and classifiers are compared based on the parameters such as performance index (PI) and quality value (QV).The performance index and quality value of code converters are at low value of 33.26% and 12.74, respectively. The highest PI of 98.03% and QV of 23.82 are attained at dB2 wavelet with hard thresholding method for SVD classifier. All the postclassifiers are settled at PI value of more than 90% at QV of 20.

show abstract

Human scalp EEG processing: Various soft computing approaches

Cited by 40 publications

References 217 publications

High performance EEG signal classification using classifiability and the Twin SVM

High performance EEG signal classification using classifiability and the Twin SVM

EEG signal classification using PSO trained RBF neural network for epilepsy identification

Performance analysis of wavelet transforms and morphological operator-based classification of epilepsy risk levels

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