Enhancing P300 wave of BCI systems via negentropy in adaptive wavelet denoising

Vahabi, Zahra; Amirfattahi, Rassoul; Mirzaei, AR

doi:10.4103/2228-7477.95354

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…Time-frequency analysis can know the instantaneous frequency and amplitude of signals at diferent moments. Common timefrequency analysis methods include empirical mode decomposition (EMD), ensemble EMD, energy entropy wavelet transform (EEWT) [35,36], empirical wavelet transform (EWT), VMD, etc. Literatures [37,38] can prove VMD's obvious advantage in number decomposition.…”

Section: Experimental Equipment Construction and Datamentioning

confidence: 99%

Intelligent Diagnosis of Bogie Traction Seat Based on PCA-OVO and SVM Optimized by Modified Arithmetic Optimization Algorithm

Liu

Chang

et al. 2022

Mathematical Problems in Engineering

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The bogie traction seat is the main part of urban rail vehicles and its fault status will affect the safe and smooth operation of the vehicles. For the low accuracy of the traditional detection methods, an intelligent fault diagnosis model of the traction seat based on principal component analysis with one versus one (PCA-OVO) and support vector machine (SVM) optimized by modified arithmetic optimization algorithm is proposed. Firstly, for the difficulty of high-frequency data collection under real working conditions, the simulation platform of the bogie of an urban rail vehicle is built, and the vibration signals of the traction seat are collected and processed in different domains, and then the feature extraction and fusion method based on PCA-OVO is used to obtain the sensitive feature set of the traction seat. Finally, the SVM intelligence recognition model is constructed based on the sensitive feature set data, and its parameters are optimally combined and selected by the modified arithmetic optimization algorithm after introducing the cosine factor. The experiments prove the effectiveness of the model. Experimental results show that the model is effective and provides a new model for fault diagnosis of traction seat of urban rail vehicles.

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Section: Experimental Equipment Construction and Datamentioning

confidence: 99%

Intelligent Diagnosis of Bogie Traction Seat Based on PCA-OVO and SVM Optimized by Modified Arithmetic Optimization Algorithm

Liu

Chang

et al. 2022

Mathematical Problems in Engineering

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“…• Wavelet de-noising and thresholding: The multi-resolution analysis is used to transfer the EEG signal to the discrete wavelet domain. The contrasting or adaptive threshold level is used to reduce particular coefficients associated with the noise signal [261]. Shorter coefficients would tend to define noise characteristics throughout time and scale in a well-matched wavelet representation.…”

Section: Signal De-noisingmentioning

confidence: 99%

Brain-Computer Interface: Advancement and Challenges

Mridha

Das

Kabir

et al. 2021

Sensors

103

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Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.

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“…In this paper, the noise is considered dependent on the signal and has a nonlinear relation to it,[ 9 ] so we employed a nonlinear noise estimation approach based on Volterra series expansion to remove the noise. Recognition and compensation of undesired nonlinearity is one of the important subjects in the field of digital signal processing.…”

Section: Introductionmentioning

confidence: 99%

Noise estimation in electroencephalogram signal by using volterra series coefficients

Hassani¹,

Karami²

2015

J Med Signals Sens

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The Volterra model is widely used for nonlinearity identification in practical applications. In this paper, we employed Volterra model to find the nonlinearity relation between electroencephalogram (EEG) signal and the noise that is a novel approach to estimate noise in EEG signal. We show that by employing this method. We can considerably improve the signal to noise ratio by the ratio of at least 1.54. An important issue in implementing Volterra model is its computation complexity, especially when the degree of nonlinearity is increased. Hence, in many applications it is urgent to reduce the complexity of computation. In this paper, we use the property of EEG signal and propose a new and good approximation of delayed input signal to its adjacent samples in order to reduce the computation of finding Volterra series coefficients. The computation complexity is reduced by the ratio of at least 1/3 when the filter memory is 3.

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Enhancing P300 wave of BCI systems via negentropy in adaptive wavelet denoising

Cited by 7 publications

References 21 publications

Intelligent Diagnosis of Bogie Traction Seat Based on PCA-OVO and SVM Optimized by Modified Arithmetic Optimization Algorithm

Intelligent Diagnosis of Bogie Traction Seat Based on PCA-OVO and SVM Optimized by Modified Arithmetic Optimization Algorithm

Brain-Computer Interface: Advancement and Challenges

Noise estimation in electroencephalogram signal by using volterra series coefficients

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