An Algorithm for Detecting the Instant of Olfactory Stimulus Perception, Using the EEG Signal and the Hilbert-Huang Transform

2017 ◽  
pp. 499-505 ◽  
Author(s):  
Edward Puchala ◽  
Maciej Krysmann
Keyword(s):  
Olfactory Stimulus ◽  
Eeg Signal ◽  
Hilbert Huang Transform ◽  
Stimulus Perception

Understanding the nonlinear behavior of EEG with advanced machine learning in artifact elimination

2021 ◽  
Author(s):  
Md Samiul Haque Sunny ◽  
Shifat Hossain ◽  
Nashrah Afroze ◽  
Md. Kamrul Hasan ◽  
Eklas Hossain ◽  
...  
Keyword(s):  
Nonlinear Behavior ◽  
Cognitive Status ◽  
Brain Diseases ◽  
Eeg Signal ◽  
Hilbert Huang Transform ◽  
Neuro Fuzzy ◽  
Research Goal ◽  
Adaptive Noise ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

Abstract Steady-state Visually Evoked Potential (SSVEP) based Electroencephalogram (EEG) signal is utilized in brain-computer interface paradigms, diagnosis of brain diseases, and measurement of the cognitive status of the human brain. However, various artifacts such as the Electrocardiogram (ECG), Electrooculogram (EOG), and Electromyogram (EMG) are present in the raw EEG signal, which adversely affect the EEG-based appliances. In this research, Adaptive Neuro-fuzzy Interface Systems (ANFIS) and Hilbert-Huang Transform (HHT) are primarily employed to remove the artifacts from EEG signals. This work proposes Adaptive Noise Cancellation (ANC) and ANFIS based methods for canceling EEG artifacts. A mathematical model of EEG with the aforementioned artifacts is determined to accomplish the research goal, and then those artifacts are eliminated based on their mathematical characteristics. ANC, ANFIS, and HHT algorithms are simulated on the MATLAB platform, and their performances are also justified by various error estimation criteria using hardware implementation.

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.

A GUI based EEG Signal Denoising using Hilbert Huang Transform

2016 ◽  
Vol 7 (1) ◽  
pp. 25
Author(s):  
PADMAJA N. ◽  
BHARATHI M. ◽  
SUJATHA E. ◽  
◽  
◽  
...  
Keyword(s):  
Signal Denoising ◽  
Eeg Signal ◽  
Hilbert Huang Transform

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 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.

scholarly journals Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Hamidreza Namazi ◽  
Amin Akrami ◽  
Sina Nazeri ◽  
Vladimir V. Kulish
Keyword(s):  
Brain Research ◽  
Approximate Entropy ◽  
Olfactory Stimulus ◽  
Brain Diseases ◽  
Eeg Signal ◽  
Olfactory Stimuli ◽  
Important Challenge ◽  
Molecular Complexity ◽  
Electroencephalogram Eeg

An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal EEG signal. Also, odorant having higher entropy causes the EEG signal to have lower approximate entropy. The method discussed here can be applied and investigated in case of patients with brain diseases as the rehabilitation purpose.

Analysis of Motor Imagery EEG Based on Hilbert-Huang Transform

2014 ◽  
Vol 998-999 ◽  
pp. 833-837
Author(s):  
Xiao Lin Zhu ◽  
Jian Ping Liu ◽  
Xiao Nan Zhang
Keyword(s):  
Motor Imagery ◽  
Recognition Rate ◽  
Eeg Signal ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Left Hand ◽  
Time Frequency ◽  
The Hilbert Transform ◽  
Electroencephalogram Eeg ◽  
Emd Method

Based on Hilbert-Huang Transform (HHT) theory, we present a method to analyze the electroencephalogram (EEG) signal of right and left hand motor imagery. Firstly, EMD method decomposed EEG signal into a group of intrinsic mode functions (IMFs). The first three IMFs were extracted to denoise. We adopt endpoint Mirror Extension method to relieve the influence on subsequent processing brought by endpoint effect. According to the Hilbert transform, we can obtain the time-frequency distribution. The energy of the first three components is selected as the input of SVM. The results show that EMD is an efficient method to analyze the EEG signal. The proposed method obtains an ideal recognition rate.

Investigation of Epileptic Seizures and Sleep Disturbance

2021 ◽  
pp. 52-70
Author(s):  
Bharath V. S. ◽  
Miraclin F. ◽  
Bhanu Priyanka ◽  
Bharath K. P. ◽  
Rajesh Kumar M.
Keyword(s):  
Neural Network ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Learning Models ◽  
Hilbert Huang Transform ◽  
Intelligent Devices ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Machine Learning Models

In this chapter, the authors make use of signal processing techniques and machine learning models to analyze the EEG signal. First, the EEG signal is broken down into the frequency sub-bands using a discrete wavelet transform (DWT). Then the kernel principle component analysis (KPCA) method is used to reduce the dimension of data. They input these extracted features into a neural network to find if the patient has an epileptic seizure or not. The results of the classification process due to artificial neural networks (ANN) are studied and analyzed. Also, to recognize the abnormal activities in the EEG signal, caused by changes in neuronal electrochemical activity in epileptic patients, the EEG signal is processed using the Hilbert Huang transform (HHT). Given the wide array of epilepsy, we need to make use of intelligent devices in the treatment of epilepsy by using the patient's neurophysiology for better diagnosis before the clinical operation.

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.

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