Time-varying model identification for time–frequency feature extraction from EEG data

Li, Yang; Wei, Hua‐Liang; Billings, S.A.; Sarrigiannis, Ptolemaios G.

doi:10.1016/j.jneumeth.2010.11.027

Cited by 37 publications

(27 citation statements)

References 33 publications

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“…Specifically, the fourth-order Daubechies (db4) wavelet is selected due to its good local approximated performance for nonstationary signals [19,24]. Five frequency sub-bands of clinical interest are then obtained by using the wavelet decomposition and reconstruction: delta (0-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13)(14)(15)(16), beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) and gamma . Herein, wavelet features of its good localizing properties are extracted from each sub-band in the time-frequency domain, followed by a well-known PCA algorithm of the dimensionality reduction in order to remove the irrelevant or spurious features.…”

Section: Methodsmentioning

confidence: 99%

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Wang

Xue

et al. 2017

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206

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Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre-processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub-bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed-up the epilepsy diagnosis.

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Section: Methodsmentioning

confidence: 99%

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Wang

Xue

et al. 2017

Entropy

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206

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“…Exploring an effective way to extract robust feature and classify EEG signals effectively is an significant work to applications of EEG. Traditionally, the process of EEG signals recognition consists of two stages [6]: a feature extraction stage, where meaningful information is extracted from the EEG recordings; a classification stage, where a decision is made from the selected features [7]. Traditional feature extraction methods mainly include frequency band analysis [8], multiscale radial basis functions [9], independent component analysis [10], continuous wavelet transform [11] ， and common spatial pattern algorithm [12] etc.…”

Section: Introductionmentioning

confidence: 99%

An Amplitudes-Perturbation Data Augmentation Method in Convolutional Neural Networks for EEG Decoding

Zhang

Lei

Yang

2018

2018 5th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)

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Brain-Computer Interface (BCI) system provides a pathway between humans and the outside world by analyzing brain signals which contain potential neural information. Electroencephalography (EEG) is one of most commonly used brain signals and EEG recognition is an important part of BCI system. Recently, convolutional neural networks (ConvNet) in deep learning are becoming the new cutting edge tools to tackle the problem of EEG recognition. However, training an effective deep learning model requires a big number of data, which limits the application of EEG datasets with a small number of samples. In order to solve the issue of data insufficiency in deep learning for EEG decoding, we propose a novel data augmentation method that add perturbations to amplitudes of EEG signals after transform them to frequency domain. In experiments, we explore the performance of signal recognition with the state-ofthe-art models before and after data augmentation on BCI Competition IV dataset 2a and our local dataset. The results show that our data augmentation technique can improve the accuracy of EEG recognition effectively.

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“…Recently, many signal analyzing and processing techniques have been proposed for studying EEG signals [2][3][4][5][6][7][8][9][10]. Among these methods, traditional Fourier spectral analysis has been used to extract features of EEG signals for detection of seizure [2,3].…”

Section: Introductionmentioning

confidence: 99%

Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals

Chai

et al. 2015

Biomedical Signal Processing and Control

147

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Time-varying model identification for time–frequency feature extraction from EEG data

Cited by 37 publications

References 33 publications

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

An Amplitudes-Perturbation Data Augmentation Method in Convolutional Neural Networks for EEG Decoding

Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals

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