EEG signal artefact removal using flower pollination fractional calculus optimisation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jayalaxmi Anem ◽  
G. Sateeshkumar ◽  
R. Madhu
Keyword(s):  
Feature Extraction ◽  
Fractional Calculus ◽  
Random Noise ◽  
Vital Role ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Content Type ◽  
Flower Pollination ◽  
Optimisation Algorithm ◽  
A New Technique

PurposeThe main aim of this paper is to design a technique for improving the quality of EEG signal by removing artefacts which is obtained during acquisition. Initially, pre-processing is done on EEG signal for quality improvement. Then, by using wavelet transform (WT) feature extraction is done. The artefacts present in the EEG are removed using deep convLSTM. This deep convLSTM is trained by proposed fractional calculus based flower pollination optimisation algorithm.Design/methodology/approachNowadays' EEG signals play vital role in the field of neurophysiologic research. Brain activities of human can be analysed by using EEG signals. These signals are frequently affected by noise during acquisition and other external disturbances, which lead to degrade the signal quality. Denoising of EEG signals is necessary for the effective usage of signals in any application. This paper proposes a new technique named as flower pollination fractional calculus optimisation (FPFCO) algorithm for the removal of artefacts from EEG signal through deep learning scheme. FPFCO algorithm is the integration of flower pollination optimisation and fractional calculus which takes the advantages of both the flower pollination optimisation and fractional calculus which is used to train the deep convLSTM. The existed FPO algorithm is used for solution update through global and local pollinations. In this case, the fractional calculus (FC) method attempts to include the past solution by including the second order derivative. As a result, the suggested FPFCO algorithm approaches the best solution faster than the existing flower pollination optimization (FPO) method. Initially, 5 EEG signals are contaminated by artefacts such as EMG, EOG, EEG and random noise. These contaminated EEG signals are pre-processed to remove baseline and power line noises. Further, feature extraction is done by using WT and extracted features are applied to deep convLSTM, which is trained by proposed fractional calculus based flower pollination optimisation algorithm. FPFCO is used for the effective removal of artefacts from EEG signal. The proposed technique is compared with existing techniques in terms of SNR and MSE.FindingsThe proposed technique is compared with existing techniques in terms of SNR, RMSE and MSE.Originality/value100%.

P300 Feature Extraction of Visual and Auditory Evoked EEG Signal

2014 ◽  
Vol 490-491 ◽  
pp. 1374-1377 ◽  
Author(s):  
Xiao Yan Qiao ◽  
Jia Hui Peng
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Evoked Potentials ◽  
Brain Computer Interface ◽  
Auditory Stimulation ◽  
Computer Interface ◽  
Eeg Signal ◽  
Significant Issue ◽  
Eeg Signals ◽  
Electroencephalogram Eeg

It is a significant issue to accurately and quickly extract brain evoked potentials under strong noise in the research of brain-computer interface technology. Considering the non-stationary and nonlinearity of the electroencephalogram (EEG) signal, the method of wavelet transform is adopted to extract P300 feature from visual, auditory and visual-auditory evoked EEG signal. Firstly, the imperative pretreatment to EEG acquisition signals was performed. Secondly, respectivly obtained approximate and detail coefficients of each layer, by decomposing the pretreated signals for five layers using wavelet transform. Finally, the approximate coefficients of the fifth layer were reconstructed to extract P300 feature. The results have shown that the method can effectively extract the P300 feature under the different visual-auditory stimulation modes and lay a foundation for processing visual-auditory evoked EEG signals under the different mental tasks.

scholarly journals Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Xiongliang Xiao ◽  
Yuee Fang
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Signal To Noise Ratio ◽  
Large Individual ◽  
Eeg Signal ◽  
Signal Recognition ◽  
Eeg Signals ◽  
Time Frequency ◽  
Morlet Wavelet Transform

Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large individual differences, EEG feature extraction and classification have the problems of low accuracy and efficiency. To solve this problem, this paper proposes a recognition method of motor imagery EEG signal based on deep convolution network. This method firstly aims at the problem of low quality of EEG signal characteristic data, and uses short-time Fourier transform (STFT) and continuous Morlet wavelet transform (CMWT) to preprocess the collected experimental data sets based on time series characteristics. So as to obtain EEG signals that are distinct and have time-frequency characteristics. And based on the improved CNN network model to efficiently recognize EEG signals, to achieve high-quality EEG feature extraction and classification. Further improve the quality of EEG signal feature acquisition, and ensure the high accuracy and precision of EEG signal recognition. Finally, the proposed method is validated based on the BCI competiton dataset and laboratory measured data. Experimental results show that the accuracy of this method for EEG signal recognition is 0.9324, the precision is 0.9653, and the AUC is 0.9464. It shows good practicality and applicability.

scholarly journals Methods for the Analysis of EEG signals: A Review

2021 ◽  
Vol 9 (9) ◽  
pp. 873-876
Author(s):  
Malika Garg
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Electrical Activity ◽  
Signal Analysis ◽  
Eeg Signal ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Processing Techniques ◽  
The Brain ◽  
Eeg Signal Analysis

Abstract: Electroencephalography (EEG) helps to predict the state of the brain. It tells about the electrical activity going on in the brain. Difference of the surface potential evolved from various activities get recorded as EEG. The analysis of these EEG signals is of utmost importance to solve the problems related to the brain. Signal pre-processing, feature extraction and classification are the main steps of the EEG signal analysis. In this article we discussed various processing techniques of EEG signals. Keywords: EEG, analysis, signal processing, feature extraction, classification

scholarly journals Best Feature Extraction and Classification Algorithms for EEG Signals in Neuromarketing

2020 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Boniadi Naieni
Keyword(s):  
Feature Extraction ◽  
Brain Activity ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
High Efficient ◽  
The Brain

Purpose: There are many methods for advertisements of products and neuromarketing is new area in this field. In neuromarketing, we use neuroscience information for revealing Consumer behavior by extracting brain activity. Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG), and Electroencephalography (EEG) are high efficient tools for investigating the brain activity in neuromarketing. EEG signal is a high temporal resolution and a cheap method for examining the brain activity. Materials and Methods: 32 subjects (16 males and 16 females) aging between 20-35 years old participated in this study. We proposed neuromarketing method exploit EEG system for predicting consumer preferences while they view E-commerce products. We apply some important preprocessing steps for noise and artifacts elimination of the EEG signal. In next step feature extraction methods are applied on the EEG data such as Discrete Wavelet Transform (DWT) and statistical features. The goal of this study is classification of analyzed EEG signal to likes and dislikes using supervised algorithms. We use Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) for data classification. The mentioned methods were used for whole and lobe brain data. Results: The results show high efficacy for SVM algorithms than other methods. Accuracy, sensitivity, specificity and precision parameters were used for evaluation of the model performance. The results show high performance of SVM algorithms for classification of the data with accuracy more than 87% and 84% for whole and parietal lobe data. Conclusion: We designed a tool with EEG signals for extraction brain activity of consumers using neuromarketing methods. We investigated the effects of advertising on brain activity of consumers by EEG signals measures.

Multi-objective flower pollination algorithm: a new technique for EEG signal denoising

2022 ◽  
Author(s):  
Zaid Abdi Alkareem Alyasseri ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Xin-She Yang ◽  
Mazin Abed Mohammed ◽  
...  
Keyword(s):  
New Technique ◽  
Flower Pollination Algorithm ◽  
Signal Denoising ◽  
Eeg Signal ◽  
Multi Objective ◽  
Flower Pollination ◽  
A New Technique

scholarly journals n-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Karen Alicia Aguilar Cruz ◽  
María Teresa Zagaceta Álvarez ◽  
Rosaura Palma Orozco ◽  
José de Jesús Medel Juárez
Keyword(s):  
Feature Extraction ◽  
Parameter Estimation ◽  
System Identification ◽  
Eeg Signal ◽  
Important Research ◽  
Forgetting Factor ◽  
Eeg Signals ◽  
Stochastic Signal ◽  
The One ◽  
Average Approximation

Electroencephalograms (EEG) signals are of interest because of their relationship with physiological activities, allowing a description of motion, speaking, or thinking. Important research has been developed to take advantage of EEG using classification or predictor algorithms based on parameters that help to describe the signal behavior. Thus, great importance should be taken to feature extraction which is complicated for the Parameter Estimation (PE)–System Identification (SI) process. When based on an average approximation, nonstationary characteristics are presented. For PE the comparison of three forms of iterative-recursive uses of the Exponential Forgetting Factor (EFF) combined with a linear function to identify a synthetic stochastic signal is presented. The one with best results seen through the functional error is applied to approximate an EEG signal for a simple classification example, showing the effectiveness of our proposal.

scholarly journals EEG Signal Research for Identification of Epilepsy using Machine Learning Classification Accession

2019 ◽  
Vol 8 (6S4) ◽  
pp. 683-688
Keyword(s):  
Feature Extraction ◽  
Cell Activity ◽  
Vital Role ◽  
Brain Cell ◽  
Svm Classifier ◽  
Eeg Signal ◽  
Machine Learning Classification ◽  
Mode Decomposition ◽  
Electroencephalogram Eeg ◽  
The Brain

Epilepsy is censorious neurological disorder in which nerve cell activity in the brain is disturbed causing recurrent seizures which are sudden, uncontrolled electrical discharges in the brain cell. In clinical treatment of epileptic patients seizure reorganization has much prominence. Hence in detecting the phenomenon of epilepsy Electroencephalogram (EEG) signal is widely used as it includes important carnal data of the brain. Though it is critical to analyze the EEG signal and identify the seizures. So feature extraction of EEG signal plays a vital role for epilepsy detection. This paper describes an worthwhile feature extraction based on variational mode decomposition (VMD) to identify epilepsy. The extracted features fed to ANN, KNN and SVM in order to classify epilepsy. The performance of the SVM classifier shows the better classification compared to existing methods.

scholarly journals Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 5 (1) ◽  
pp. 35-44
Author(s):  
Suwanto Suwanto ◽  
M. Hasan Bisri ◽  
Dian Candra Rini Novitasari ◽  
Ahmad Hanif Asyhar
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Inference System ◽  
Neuro Fuzzy ◽  
The Brain

Epilepsy is a disease that attacks the brain and results in seizures due to neurological disorders. The electrical activity of the brain recorded by the EEG signal test, because EEG test can be used to diagnose brain and mental diseases such as epilepsy. This study aims to identify whether a person has epilepsy or not along with the result of accurate, sensitivity, and precision rate using Fast Fourier Transform (FFT) and Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The FFT is used to transform EEG signals from time-based into frequency-based and continued with feature extraction to take characteristics from each filtering signal using the median, mean, and standard deviations of each EEG signal. The results of the feature extraction used for input on the category process based on characteristics data (classification) using ANFIS. EEG signal data is obtained from epilepsy center online database of Bonn University, German. The results of the EEG signal classification system using ANFIS with two classes (Normal-Epilepsy) states accuracy, sensitivity, and precision of 100%. The classification systems with three class division (Normal-Not Seizure Epilepsy-Epilepsy) resulted in an accuracy of 89.33% sensitivity of 89.37% and precision of 89.33%.

scholarly journals Performance Analysis of IIR & FIR Windowing Techniques in Electroencephalography Signal Processing

2019 ◽  
Vol 8 (10) ◽  
pp. 3568-3578
Keyword(s):  
Performance Analysis ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Data Retrieval ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Signal To Noise ◽  
Noise Ratio

A very small amplitude (μV) of the electroencephalography (EEG) signal is infected by diverse artifacts. These artifacts have an effect on the distinctiveness of the signal because of which medical psychoanalysis and data retrieval is difficult. Therefore, EEG signals are initially preprocessed to eliminate the artifacts to produce signals that can serve as a base for further processing and analysis. Different filters are implemented to eliminate the artifacts present in the EEG signal. Recent research shows that window technique Finite Impulse Response (FIR) filter is usually used. In this paper, digital Infinite Impulse Response (IIR) filter and different Finite Impulse Response (FIR) window filters (Hanning, Hamming, Kaiser, Blackman) of various orders are implemented to eradicate the random noise added to EEG signals. Their performance analysis has been done in Matlab (R2016a) by calculating the mean square error, mean absolute error, signal to noise ratio, peak signal to noise ratio and cross-correlation. The results show that Kaiser Window based finite impulse response filter outperforms in removing the noise from the electroencephalogram signal. This research focuses on eradicating random noise in electroencephalogram signals but this approach will be extended to a different source of electroencephalogram contamination.

scholarly journals A Review of Processing Methods and Classification Algorithm for EEG Signal

2020 ◽  
Vol 13 (3) ◽  
pp. 23-29
Author(s):  
Yu Xie ◽  
Stefan Oniga
Keyword(s):  
Feature Extraction ◽  
Classification Algorithm ◽  
Eeg Signal ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Research Directions ◽  
Processing Methods ◽  
Computer Interfaces ◽  
Analysis Technique ◽  
Processing Techniques

AbstractThe analysis technique of EEG signals is developing promptly with the evolution of Brain Computer- Interfaces science. The processing and classification algorithm of EEG signals includes three states: pre-processing, feature extraction and classification. The article discusses both conventional and recent processing techniques of EEG signals at the phases of preprocessing, feature extraction and classification. Finally, analyze popular research directions in the future.

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