EEG Signal Processing Based on Genetic Algorithm for Extracting Mixed Features

2019 ◽  
Vol 33 (06) ◽  
pp. 1958008 ◽  
Author(s):  
Yanping Li ◽  
Linyan Wu ◽  
Tao Wang ◽  
Nuo Gao ◽  
Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Laboratory Data ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Hilbert Huang Transform ◽  
Online Test ◽  
Mixed Features ◽  
Data Credibility ◽  
Eeg Signal Processing

In order to improve the classification of motor imagery EEG accuracy, this paper proposes a method based on Genetic Algorithm (GA) EEG signal classification method to extract mixed characteristics. This method uses wavelet analysis and Hilbert–Huang Transform (HHT) to analyze EEG signals and optimizes the characteristics through Common Spatial Patterns (CSP). Finally, the 14 sub features are optimized by GA, and the weights and data credibility of different sub features are obtained. The experiment was tested with 2003BCI competition data and the EEG signal collected by the laboratory. The accuracy rate of competition data was increased from about 75% before weighting to more than 80% after weighting, and the laboratory data increased from about 65% before weighting to about 75% after weighting. Experimental results show that this method can effectively improve the classification accuracy of EEG signals, and the most useful EEG signals can be extracted from large amounts of data for feature extraction and classification. Finally, the online test is carried out to further verify the feasibility of the method.

scholarly journals Epileptic Seizures Detection based on Empirical Mode Decomposition and Hilbert-Huang transform of EEG Signal

2015 ◽  
Vol 1 (2) ◽  
pp. 295
Author(s):  
Mokhtar Mohammadi ◽  
Aso M. Darwesh
Keyword(s):  
Empirical Mode Decomposition ◽  
Epileptic Seizure ◽  
Spectral Energy ◽  
Support Vector ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition

The electrical activities of brain fluctuate frequently and can be analyzed using electroencephalogram (EEG) signals. We present a new method for classification of ictal and seizure-free intracranial EEG recordings. The proposed method uses the application of multivariate empirical mode decomposition (MEMD) algorithm combines with the Hilbert transform as the Hilbert-Huang transform (HHT) and analyzing spectral energy of the intrinsic mode function of the signal. EMD uses the characteristics of signals to adaptively decompose them to several intrinsic mode functions (IMFs). Hilbert transforms (HTs) are then used to transform the IMFs into instantaneous frequencies (IFs), to obtain the signals time-frequency-energy distributions. Classification of the EEG signal that is epileptic seizure exists or not has been done using support vector machine. The algorithm was tested in 6 intracranial channels EEG records acquired in 9 patients with refractory epilepsy and validated by the Epilepsy Center of the University Hospital of Freiburg. The experimental results show that the proposed method efficiently detects the presence of epileptic seizure in EEG signals and also showed a reasonable accuracy in detection.

Classification of the emotional states based on the EEG signal processing

2009 ◽  
Author(s):  
Martin Macas ◽  
Michal Vavrecka ◽  
Vaclav Gerla ◽  
Lenka Lhotska
Keyword(s):  
Signal Processing ◽  
Emotional States ◽  
Eeg Signal ◽  
Eeg Signal Processing

scholarly journals Data Mining Based Approach for Evaluation of EEG Signals for Epilepsy Detection

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Physiological Signals ◽  
Alternative Formulation ◽  
Eeg Signal ◽  
Detection Problem ◽  
Eeg Signals ◽  
Epileptic Patients ◽  
To Come

The objective of this proposed research is to come up with a general methodology for classification of time series events, and to apply that methodology to the analysis of physiological signals recorded from epileptic patients for seizure analysis depending on EEG signal. In contrast to previous works, this research considered an alternative formulation of seizure analysis as a detection problem. This approach offers a good treatment of seizure detection

Application of the Hilbert_Huang Transform and Wavelet Transform in the EEG Processing

2013 ◽  
Vol 756-759 ◽  
pp. 1753-1757
Author(s):  
Gui Xin Zhang ◽  
Ping Dong Wu ◽  
Man Ling Huang
Keyword(s):  
Wavelet Transform ◽  
Brain Activity ◽  
Eeg Signal ◽  
Hilbert Huang Transform ◽  
Signal Processing Method ◽  
Communication Method ◽  
Machine Interface ◽  
And Control ◽  
The Brain ◽  
Eeg Signal Processing

Brain-Machine Interface (BMI) could make people control machine through EEG which is produced by the brain activity, and it provide a new communication method between human and machine. The research for BMI will extend the ability of communication and control the environment and machine. The key point of the BMI is how to abstract and distinguish different EEG characters. Therefore, EEG signal processing method is the emphasis of BMI. Wavelet Transform and Hilbert-Huang Transform are used to analyze the EEG signal in this paper. The results indicate that these two methods could abstract the main characters of the EEG, but the Hilbert-Huang Transform could express the distributing status in the time and frequency aspect of the EEG more accurately, because it produces the self-adaptive basis according the data, and obtain the local and instantaneous frequency of the EEG.

CLASSIFICATION OF ICTAL EEG USING MODELING BASED SPECTRAL AND TEMPORAL FEATURES ON INSTANTANEOUS AMPLITUDE-FREQUENCY COMPONENTS OF IMFs

2018 ◽  
Vol 30 (06) ◽  
pp. 1850042 ◽  
Author(s):  
K. S. Biju ◽  
M. G. Jibukumar
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Classification Accuracy ◽  
Spectral Features ◽  
Eeg Signal ◽  
Instantaneous Amplitude ◽  
Eeg Signals ◽  
Hilbert Huang Transform ◽  
Temporal Features ◽  
Power Spectral

In the present study, a method for classifying the different ictal stages in electroencephalogram (EEG) signals is proposed. The main symptoms of epilepsy are indicated by ictal activities, which trigger widespread neurological disorders other than stroke and thus affect the world population. In this work, a novel ictal classification method that combines the spectral and temporal features of twin components in Hilbert–Huang transform is proposed. Spectral features of instantaneous amplitude (IA) function are obtained based on the power spectral density of autoregressive (AR) modeling. Here four different cases of ictal activities of EEG signal are classified. In each case first and second intrinsic mode function of Hilbert–Huang transform are tabulated. The power spectral density of AR(6) and AR(10) model are done for IA1 and IA2 components of each case. Temporal features of either instantaneous frequency (IF) function or IA are computed. The feature vectors are tested in a well-known database of different classes in interictal, ictal, and normal activities of EEG signals. The discriminating power of each vector is evaluated through one-way analysis of variance, and the classification results are verified using an artificial neural network (ANN) classifier. The performance of the classifier was assessed in term of sensitivity, specificity, and total classification accuracy. The spectral features of the AR(10) of IA and the temporal features of IA yielded 100% accuracy, 100% sensitivity, and 100% specificity in the ictal classification. By contrast, these features obtained only 83.33% of the total classification accuracy in ictal and interictal EEG signal.

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.

scholarly journals Anomalous pattern based clustering of mental tasks with subject independent learning – some preliminary results

2012 ◽  
Vol 1 (1) ◽  
pp. 55 ◽  
Author(s):  
Renato Amorim ◽  
Boris Mirkin ◽  
John Q. Gan
Keyword(s):  
Power Spectrum Density ◽  
Independent Learning ◽  
Signal Classification ◽  
Eeg Signal ◽  
Mental Task ◽  
Eeg Signals ◽  
Task Classification ◽  
Spectrum Density ◽  
Anomalous Pattern

In this paper we describe a new method for EEG signal classification in which the classification of one subject’s EEG signals is based on features learnt from another subject. This method applies to the power spectrum density data and assigns class-dependent information weights to individual features. The informative features appear to be rather similar among different subjects, thus supporting the view that there are subject independent general brain patterns for the same mental task. Classification is done via clustering using the intelligent k-means algorithm with the most informative features from a different subject. We experimentally compare our method with others.

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