Frequency analysis of eyes open and eyes closed EEG signals using the Hilbert-Huang Transform

2012 ◽  
Author(s):  
R. A. Thuraisingham ◽  
Y. Tran ◽  
A. Craig ◽  
H. Nguyen
Keyword(s):  
Frequency Analysis ◽  
Eeg Signals ◽  
Hilbert Huang Transform ◽  
Eyes Closed ◽  
Eyes Open

scholarly journals Detection of Heart Disease Risk Utilizing Correlation Matrix, Random Forest and Permutation Feature Importance Approaches

2020 ◽  
pp. 29-34
Author(s):  
Sude Pehlivan ◽  
Yalcin Isler
Keyword(s):  
Disease Risk ◽  
Wavelet Packet ◽  
Eeg Signals ◽  
Eyes Closed ◽  
The People ◽  
Low Pass ◽  
Eyes Open ◽  
Surface Eeg ◽  
Heart Disease Risk ◽  
High Pass

Surface EEG measurements that can be performed in hospitals and laboratories have reached a wearable and portable level with the development of today's technologies. Artificial intelligence-assisted brain-computer interface (BCI) systems play an important role in individuals with disabilities to process EEG signals and interact with the outside world. In particular, the research is becoming widespread to meet the basic needs of individuals in need of home care with an increasing population. In this study, it is aimed to design the BCI system that will detect the hunger and satiety status of the people on the computer platform through EEG measurements. In this context, a database was created by recording EEG signals with eyes open and eyes closed by 20 healthy participants in the first stage of the study. The noise of the EEG signal is eliminated by using a low pass, high pass, and notch filters. In the classification, using Wavelet Packet Transform (WPT) with Coiflet 1 and Daubechies 4 wavelets, 77.50% accuracy was achieved in eyes closed measurement, and 81% in eyes open measurement.

Nonlinear Dynamic Analysis of EEG Using a Stochastic Duffing-van der Pol Oscillator Model

2014 ◽  
Author(s):  
Parham Ghorbanian ◽  
Subramanian Ramakrishnan ◽  
Alan Whitman ◽  
Hashem Ashrafiuon
Keyword(s):  
Nonlinear Dynamic ◽  
Nonlinear Dynamic Analysis ◽  
Van Der Pol Oscillator ◽  
Eeg Signals ◽  
Van Der Pol ◽  
Modeling And Analysis ◽  
Eyes Closed ◽  
Nonlinear Dynamic Characteristics ◽  
Eyes Open ◽  
Eeg Recordings

In this work, we model electroencephalography (EEG) signals as the stochastic output of a double Duffing - van der Pol oscillator networks. We develop a novel optimization scheme to match data generated from the model with clinically obtained EEG data from subjects under resting eyes-open (EO) and eyes-closed (EC) conditions and derive models with outputs that show very good agreement with EEG signals in terms of both frequency and information contents. The results, reinforced by statistical analysis, shows that the EEG recordings under EC and EO resting conditions are distinct realizations of the same underlying model occurring due to parameter variations. Furthermore, the EC and EO EEG signals each exhibit distinct nonlinear dynamic characteristics. In summary, it is established that the stochastic coupled nonlinear oscillator network can provide a useful framework for modeling and analysis of EEG signals that are recorded under variety of conditions.

scholarly journals Fronto-Temporal Analysis of EEG Signals of Patients with Depression: Characterisation, Nonlinear Dynamics and Surrogate Analysis

2020 ◽  
Author(s):  
Subha D. Puthankattil
Keyword(s):  
Time Series ◽  
Unipolar Depression ◽  
Temporal Analysis ◽  
Mental States ◽  
Eeg Signals ◽  
Biosignal Analysis ◽  
Eyes Closed ◽  
Eyes Open ◽  
Eeg Recordings ◽  
The Brain

The recent advances in signal processing techniques have enabled the analysis of biosignals from brain so as to enhance the predictive capability of mental states. Biosignal analysis has been successfully used to characterise EEG signals of unipolar depression patients. Methods of characterisation of EEG signals and the use of nonlinear parameters are the major highlights of this chapter. Bipolar frontopolar-temporal EEG recordings obtained under eyes open and eyes closed conditions are used for the analysis. A discussion on the reliability of the use of energy distribution and Relative Wavelet Energy calculations for distinguishing unipolar depression patients from healthy controls is presented. The potential of the application of Wavelet Entropy to differentiate states of the brain under normal and pathologic condition is introduced. Details are given on the suitability of ascertaining certain nonlinear indices on the feature extraction, assuming the time series to be highly nonlinear. The assumption of nonlinearity of the measured EEG time series is further verified using surrogate analysis. The studies discussed in this chapter indicate lower values of nonlinear measures for patients. The higher values of signal energy associated with the delta bands of depression patients in the lower frequency range are regarded as a major characteristic indicative of a state of depression. The chapter concludes by presenting the important results in this direction that may lead to better insight on the brain activity and cognitive processes. These measures are hence posited to be potential biomarkers for the detection of depression.

scholarly journals Development of an EEG signal analysis application through a convolution of a complex Morlet wavelet: preliminary results

2019 ◽  
Vol 8 (2) ◽  
pp. B1-1-B1-19
Author(s):  
José Humberto Trueba Perdomo ◽  
◽  
Ignacio Herrera Aguilar ◽  
Francesca Gasparini ◽  
◽  
...  
Keyword(s):  
Morlet Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Eyes Open ◽  
Analysis Application ◽  
The Subject ◽  
Complex Morlet Wavelet ◽  
Electroencephalogram Eeg ◽  
Eeg Signal Analysis

This paper presents a new application for analyzing electroencephalogram (EEG) signals. The signals are pre-filtered through MATLAB's EEGLAB tool. The created application performs a convolution between the original EEG signal and a complex Morlet wavelet. As a final result, the application shows the signal power value and a spectrogram of the convoluted signal. Moreover, the created application compares different EEG channels at the same time, in a fast and straightforward way, through a time and frequency analysis. Finally, the effectiveness of the created application was demonstrated by performing an analysis of the alpha signals of healthy subjects, one signal created by the subject with eyes closed and the other, with which it was compared, was created by the same subject with eyes open. This also served to demonstrate that the power of the alpha band of the closed-eyed signal is higher than the power of the open-eyed signal.

scholarly journals EEG study of visual reactivity in aphasic patients

2013 ◽  
Vol 60 (3) ◽  
pp. 45-56
Author(s):  
Biljana Stojanovic ◽  
Ljubomir Djurasic ◽  
Stevan Jovic ◽  
Dalibor Paspalj
Keyword(s):  
Frequency Analysis ◽  
Healthy Subjects ◽  
Speech Therapy ◽  
Quantitative Eeg ◽  
Brain Regions ◽  
Rehabilitative Treatment ◽  
Quality Of Recovery ◽  
Eyes Closed ◽  
Coefficients Of Variation ◽  
Eyes Open

AIM: to compare patients with good and poor recovery after 1 and 3 months from onset of poststroke aphasia and to correlate the quality of recovery with quantitative EEG (QEEG) measures (frequency analysis with the limits of variability, and index of asymmetry). METHODS: The investigation was performed on the sample of 32 patients with poststroke aphasia, 15 females (46.88%) and 17 males (53.12%), mean age + standard deviation (SD) being 50.65+9.93 years. QEEG measures of this sample were compared with those in a group of 86 healthy controls, 39 (45.35%) females and 47 (54,65%) males, mean age +SD being 51.08+10.08 years. Frequency analysis was performed in eyes closed and eyes open conditions in both controls and in aphasics who were tested just before and two month after rehabilitative treatment with speech therapy. RESULTS: We have got normal distribution for all derivations and all frequency bands in the group of healthy subjects. On the basis of this finding, we determined coefficients of variation in patients with poststroke aphasia and discovered that their maximal variability scores were significantly decreased. Compared to healthy subjects, the index of asymmetry between two hemispheres and between main brain regions was significantly higher in the aphasic patients than in controls. However, the differences in the index of asymmetry and limits of variability significantly decreased after two month treatment in the subgroup of patients with good improvement compared with the subgroup of patients with poor improvement of poststroke aphasia. CONCLUSION: QEEG measures may have predicitive value in post-stroke aphasia.

scholarly journals Towards a minimal EEG channel array for a biometric system using resting-state and a genetic algorithm for channel selection

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Luis Alfredo Moctezuma ◽  
Marta Molinas
Keyword(s):  
Genetic Algorithm ◽  
Resting State ◽  
Channel Selection ◽  
Discrete Wavelet ◽  
Biometric System ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Minimal Subset ◽  
Eyes Open ◽  
The Subject

Abstract We present a new approach for a biometric system based on electroencephalographic (EEG) signals of resting-state, that can identify a subject and reject intruders with a minimal subset of EEG channels. To select features, we first use the discrete wavelet transform (DWT) or empirical mode decomposition (EMD) to decompose the EEG signals into a set of sub-bands, for which we compute the instantaneous and Teager energy and the Higuchi and Petrosian fractal dimensions for each sub-band. The obtained features are used as input for the local outlier factor (LOF) algorithm to create a model for each subject, with the aim of learning from it and rejecting instances not related to the subject in the model. In search of a minimal subset of EEG channels, we used a channel-selection method based on the non-dominated sorting genetic algorithm (NSGA)-III, designed with the objectives of minimizing the required number EEG channels and increasing the true acceptance rate (TAR) and true rejection rate (TRR). This method was tested on EEG signals from 109 subjects of the public motor movement/imagery dataset (EEGMMIDB) using the resting-state with the eyes-open and the resting-state with the eyes-closed. We were able to obtain a TAR of $$1.000 \pm 0.000$$ 1.000 ± 0.000 and TRR of $$0.998 \pm 0.001$$ 0.998 ± 0.001 using 64 EEG channels. More importantly, with only three channels, we were able to obtain a TAR of up to $$0.993 \pm 0.01$$ 0.993 ± 0.01 and a TRR of up to $$0.941 \pm 0.002$$ 0.941 ± 0.002 for the Pareto-front, using NSGA-III and DWT-based features in the resting-state with the eyes-open. In the resting-state with the eyes-closed, the TAR was $$0.997 \pm 0.02$$ 0.997 ± 0.02 and the TRR $$0.950 \pm 0.05,$$ 0.950 ± 0.05 , also using DWT-based features from three channels. These results show that our approach makes it possible to create a model for each subject using EEG signals from a reduced number of channels and reject most instances of the other 108 subjects, who are intruders in the model of the subject under evaluation. Furthermore, the candidates obtained throughout the optimization process of NSGA-III showed that it is possible to obtain TARs and TRRs above 0.900 using LOF and DWT- or EMD-based features with only one to three EEG channels, opening the way to testing this approach on bigger datasets to develop a more realistic and usable EEG-based biometric system.

scholarly journals An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3474 ◽  
Author(s):  
Hai Hu ◽  
Shengxin Guo ◽  
Ran Liu ◽  
Peng Wang
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Signal Amplitude ◽  
Infinite Impulse Response ◽  
Eeg Signals ◽  
Singular Spectrum ◽  
Eyes Closed ◽  
Artifacts Removal ◽  
Eeg Data ◽  
Eyes Open

Artifacts removal and rhythms extraction from electroencephalography (EEG) signals are important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reconstructed components as artifacts and removes them. The remaining reconstructed components are then grouped based on their peak frequencies in the Fourier transform to extract the desired rhythms. The grouping rule thus enables SSA to be adaptive to EEG signals containing different levels of artifacts and rhythms. The simulated EEG data based on the Markov Process Amplitude (MPA) EEG model and the experimental EEG data in the eyes-open and eyes-closed states were used to verify the adaptive SSA method. Results showed a better performance in artifacts removal and rhythms extraction, compared with the wavelet decomposition (WDec) and another two recently reported SSA methods. Features of the extracted alpha rhythms using adaptive SSA were calculated to distinguish between the eyes-open and eyes-closed states. Results showed a higher accuracy (95.8%) than those of the WDec method (79.2%) and the infinite impulse response (IIR) filtering method (83.3%).

scholarly journals EEG Based Biometric Identification Using Correlation and MLPNN Models

2019 ◽  
Vol 15 (10) ◽  
pp. 77 ◽  
Author(s):  
T Waili ◽  
Md Gapar Md Johar ◽  
K. A. Sidek ◽  
N. S. H. Mohd Nor ◽  
H. Yaacob ◽  
...  
Keyword(s):  
Cross Correlation ◽  
Biometric Identification ◽  
Correlation Model ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Power Spectral ◽  
Significant Difference ◽  
Eyes Open ◽  
Order Butterworth Filter ◽  
Electroencephalogram Eeg

<p class="0abstract">This study investigates the capability of electroencephalogram (EEG) signals to be used for biometric identification. In the context of biometric, recently, researchers have been focusing more on biomedical signals to substitute the biometric modalities that are being used nowadays as the signals obtained from our bodies is considered more secure and privacy-compliant. The EEG signals of 6 subjects were collected where the subjects were required to undergo two baseline experiments which are, eyes open (EO) and eyes closed (EC). The signals were processed using a 2nd order Butterworth filter to eliminate the unwanted noise in the signals. Then, Daubechies (db8) wavelet was applied to the signals in the feature extraction stage and from there, Power Spectral Density (PSD) of alpha and beta waves was computed. Finally, the correlation model and Multilayer Perceptron Neural Network (MLPNN) was applied to classify the EEG signals of each subject.  Correlation model has yielded great significant difference of coefficient between autocorrelation and cross-correlation where it gives the coefficient value of 1 for autocorrelation and the coefficient value of less than 0.35 for cross-correlation. On the other hand, the MLPNN model gives an accuracy of 75.8% and 71.5% for classification during EO and EC baseline condition respectively. Therefore, these results support the usability of EEG signals in biometric recognition.</p>

Sign in / Sign up

Export Citation Format

Share Document