Automated Classification of Focal and Non-Focal EEG Signals Based on Bivariate Empirical Mode Decomposition

Sharma, Rajeev; Pachori, Ram Bilas

doi:10.4018/978-1-5225-2829-6.ch002

Cited by 14 publications

(7 citation statements)

References 53 publications

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“…The comparison with some state-ofthe-art methods has been presented in Table 6. The classification ACC has been achieved with 99%, which is superior to the considered methods in [5,7,25,26,30] shown in the table using the same Bern-Barcelona dataset for identifying focal and NF EEG signals. From the overall observation, it is concluded that the presented approach in this study is suitable and promising and can be treated as a supporting system for EEG signal classification.…”

Section: Classification Results and Discussionmentioning

confidence: 90%

“…proposed approach 100 using Bern-Barcelona dataset (focal versus NF EEG signal classification) focal versus NF Sharma et al [25] entropy measures form 10 IMFs + LSSVM 87.01 Sharma et al [26] bivariate EMD + amplitude bandwidth, precession bandwidth, and deformation bandwidth features 89.5…”

Section: Conclusion and Future Scopementioning

confidence: 99%

“…For experimental analysis and the model study, EEG datasets are collected from Bonn University Epilepsy Centre by Dr Ralph Andrzejak [47] and Bern‐Barcelona University [26]. These datasets are readily available and the maximum possible information related to these datasets are collected from the sources.…”

Section: Eeg Data Description Decomposition With Emd and Feature mentioning

confidence: 99%

“…Sharma et al [25] have applied EMD-based IMFs with the LSSVM classifier to discriminate focal and NF classes using Bern-Barcelona data. Using the same data, Sharma et al [26] proposed bivariate EMD to find IMFs and further obtained amplitude bandwidth, precession bandwidth, and deformation bandwidth for focal and NF class identification. In [27], EMD has been used to generate amplitude as well as frequency modulated signals and these features have been used with LSSVM for better discrimination of seizure and seizure-free signals in comparison with the method proposed in [28].…”

Section: Introductionmentioning

confidence: 99%

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Multi‐kernel‐based random vector functional link network with decomposed features for epileptic EEG signal classification

Parija

Dash

Bisoi

2020

IET signal process.

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This study proposes an improved hybrid model built with empirical mode decomposition (EMD) features combined with weighted multi-kernel random vector functional link network (WMKRVFLN) where the kernel parameters are optimised with an efficient optimisation algorithm known as water cycle algorithm (WCA) for diagnosis and classification of epileptic electroencephalogram (EEG) signals. The proposed model with optimisation is known as WCA-EMD-WMKRVFLN. The tanh and wavelet kernel functions are contributing together to the effectiveness of the proposed model. The features generated from EMD in terms of intrinsic mode functions (IMFs) are modulated to find important statistical and entropy based features and these features in a reduced form are employed as inputs to the model to classify epileptic EEG signals. The presented approach is evaluated in terms of percentage correct classification accuracy (ACC), specificity and sensitivity using two datasets and is compared with different classifiers and state-of-the-art techniques. The highest accuracies of 99.69% (five-class) and 100% (three-class) achieved using the Bonn-University dataset and 99.0% ACC (two-class) achieved using the Bern-Barcelona dataset. The achieved results report that the presented approach is a promising approach for EEG signal classification and is superior to several state-of-the-art techniques and is highly comparable to many such techniques.

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Section: Classification Results and Discussionmentioning

confidence: 90%

Section: Conclusion and Future Scopementioning

confidence: 99%

Section: Eeg Data Description Decomposition With Emd and Feature mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi‐kernel‐based random vector functional link network with decomposed features for epileptic EEG signal classification

Parija

Dash

Bisoi

2020

IET signal process.

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“…In recent times, empirical mode decomposition (EMD) has shown potential as a very promising decomposition technique to analyse EEG signals [11]. It has been used for the classification of epileptic EEG signals [20,19] and motor imagery BCI classification problems [5]. However, the single channel EMD method has some potential issues such as mode-mixing and frequency localisation problems.…”

Section: Introductionmentioning

confidence: 99%

Comparison Analysis: Single and Multichannel EMD-Based Filtering with Application to BCI

Gaur

Kaushik

Pachori

et al. 2018

Advances in Intelligent Systems and Computing

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A brain-computer interface (BCI) aims to facilitate a new communication path that translates the motion intentions of a human into control commands using brain signals such as magnetoencephalography (MEG) and electroencephalogram (EEG). In this work, a comparison of features obtained using single channel and multichannel empirical mode decomposition (EMD) based filtering is done to classify the multi-direction wrist movements based MEG signals for enhancing a brain-computer interface (BCI). These MEG signals are presented as a dataset 3 as part of the BCI competition IV. These single channel and multichannel EMD methods decompose MEG signals into a group of intrinsic mode functions (IMFs). The mean frequency measure of these IMFs has been used to combine these IMFs to obtain enhanced MEG signals which have major contributions from the low-frequency band (<15 Hz). The shrinkage covariance matrix has been computed as a feature set. These features have been used for the classification of MEG signals into multidirection wrist movements using the Riemannian geometry classification method. Significant improvement of > 8 % in the test stage using the multichannel EMD based filtering and >4 % when compared with single channel EMD method and BCI competition winner respectively. This analysis offers evidence that the multichannel EMD based filtering has the potential to be used in online BCI systems which facilitate a broad use of noninvasive BCIs.

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Detection and classification of electroencephalogram signals for epilepsy disease using machine learning methods

Srinath

Gayathri

2020

Int J Imaging Syst Tech

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The electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. This study describes an automated classification of EEG signal for the detection of Epilepsy disease using soft computing methods. The proposed method is comprised of three modules: (a) transformation, (b) feature computation, and (c) feature classifications. In the first module, the nonsubsampled contourlet transform is applied on the EEG signal which decomposes the signal into approximate and directional subbands. The decomposition is done using nonsubsampled pyramid filter bank and nonsubsampled directional filter bank respectively. Secondly, the statistical features are extracted from the decomposed directional subbands using wavelet packet decomposition method. Finally, these features are classified by adaptive neuro‐Fuzzy inference system classification method, which classifies the EEG signal into either focal or nonfocal signal. The proposed method is tested on a set of EEG signals for validation. The average classification rate of the proposed EEG signal classification system is 99.4%. The proposed EEG signal classification methodology achieves a sensitivity of 99.7%, a specificity of 99.7%, and an accuracy of 99.4%. The results confirmed that the proposed method has a potential in the classification of EEG signals and thereby could further improve the diagnosis of epilepsy.

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Automated Classification of Focal and Non-Focal EEG Signals Based on Bivariate Empirical Mode Decomposition

Cited by 14 publications

References 53 publications

Multi‐kernel‐based random vector functional link network with decomposed features for epileptic EEG signal classification

Multi‐kernel‐based random vector functional link network with decomposed features for epileptic EEG signal classification

Comparison Analysis: Single and Multichannel EMD-Based Filtering with Application to BCI

Detection and classification of electroencephalogram signals for epilepsy disease using machine learning methods

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