EEG signal analysis using FB expansion and second-order linear TVAR process

Pachori, Ram Bilas; Sircar, Pradip

doi:10.1016/j.sigpro.2007.07.022

Cited by 125 publications

(44 citation statements)

References 7 publications

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“…EMD is suitable for the decomposition of the EEG signals that exhibit a nonlinear and a non-stationary nature [67,68]. It separates the fast oscillations from the slow oscillations present in the signals [69].…”

Section: Discussionmentioning

confidence: 99%

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Sharma

Pachori

Acharya

2015

Entropy

287

175

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The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD) method to extract intrinsic mode functions (IMFs). The entropy features, namely, average Shannon entropy (ShEn Avg ), average Renyi's entropy (RenEn Avg ), average approximate entropy (ApEn Avg ), average sample entropy (SpEn Avg ) and average phase entropies (S1 Avg and S2 Avg ), are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM) classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct.

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Section: Discussionmentioning

confidence: 99%

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Sharma

Pachori

Acharya

2015

Entropy

287

175

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“…However, unlike the sinusoidal basis functions in the Fourier series, the Bessel functions decay over time. This feature of the Bessel functions make the FB series expansion suitable for nonstationary signals [15,[17][18][19][20][21][22][23].…”

Section: Fourier-bessel Series Expansionmentioning

confidence: 99%

Time-frequency analysis using time-order representation and Wigner distribution

Pachori

Sircar

2008

TENCON 2008 - 2008 IEEE Region 10 Conference

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Abstract-A new method for time-frequency analysis of a signal, which combines the time-order representation and the Wigner-Ville distribution (WVD) has been presented in this paper. The time-order representation based on short-term FourierBessel (FB) expansion, decomposes a multicomponent signal into a number of monocomponent signals, and then the WVD technique is applied on each component of the composite signal to analyze its time-frequency distribution (TFD). Simulation results with real bat signal are included to illustrate the effectiveness of the proposed method.

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“…There is a one-to-one correspondence between the frequency content of the signal and the order m where the coefficient attains peak magnitude (Pachori & Sircar, 2008). Note that the FB series coefficients C m are unique for a given signal x(t), similar to the Fourier coefficients.…”

Section: Fourier-bessel (Fb) Expansionmentioning

confidence: 99%

“…Recently, Fourier-Bessel (FB) expansion has been introduced as a suitable technique for non-stationary signal analysis because of that it has unique coefficients for a given signal and the Bessel functions are aperiodic and decay over time (Pachori & Sircar, 2008). FB expansion has been widely used for performing speech-related applications such as speech enhancement, speaker identification, speech recognition and synthesis, etc.…”

Section: Introductionmentioning

confidence: 99%

An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy ARTMAP

Tran

Althobiani

Ball

et al. 2013

Expert Systems with Applications

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The start-up transient signals have been widely used for fault diagnosis of induction motor because they can reveal early defects in the development process, which are not easily detected with the signals in the steady state operation. However, transient signals are non-linear and contain multi components which need a suitable technique to process and identify the fault pattern. In this paper, the fault diagnosis problem of induction motor is conducted by a data driven framework where the Fourier-Bessel (FB) expansion is used as a tool to decompose transient current signal into series of single components. For each component, the statistical features in the time and the frequency domains are extracted to represent the characteristics of motor condition. The high dimensionality of the feature set is solved by generalized discriminant analysis (GDA) implementation to decrease the computational complexity of classification. In the meantime, with the aid of GDA, the separation of the feature clusters is increased, which enables the more classification accuracy to be achieved. Finally, the reduced dimensional features are used for classifier to perform the fault diagnosis results. The classifier used in this framework is the simplified fuzzy ARTMAP (SFAM) which belongs to a special class of neural networks (NNs) and provides a lower training time in comparison to other traditional NNs. The proposed framework is validated with transient current signals from an induction motor under different conditions including bowed rotor, broken rotor bar, eccentricity, faulty bearing, mass unbalance and phase unbalance. Additionally, this paper provides the comparative performance of (i) SFAM and support vector machine (SVM), (ii) SVM in the framework and SVM combined with wavelet transform in previous studies, (iii) the use of FB decomposition and Hilbert transform decomposition. The results show that the proposed diagnosis framework is capable of significantly improving the classification accuracy.

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EEG signal analysis using FB expansion and second-order linear TVAR process

Cited by 125 publications

References 7 publications

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Time-frequency analysis using time-order representation and Wigner distribution

An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy ARTMAP

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