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.

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.

Visual Imagery Classification Using Shapelets of EEG Signals

2012 ◽  
Author(s):  
Stewart Contreras ◽  
V. Sundararajan
Keyword(s):  
Time Series ◽  
Classification Scheme ◽  
Signal To Noise Ratio ◽  
Event Related Potential ◽  
Eeg Signals ◽  
Classification Rate ◽  
Average Classification Accuracy ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
The Brain

The goal of this paper is to reconstruct three primitive shapes — rectangular cube, cone and cylinder — by analyzing electrical signals which are emitted by the brain. Three participants are asked to visualize these shapes. During visualization, a 14-channel neuroheadset is used to record electroencephalogram (EEG) signals along the scalp. The EEG recordings are then averaged to increase the signal to noise ratio which is referred to as an event related potential (ERP). Every possible subsequence of each ERP signal is analyzed in an attempt to determine a time series which is maximally representative of a particular class. These time series are referred to as shapelets and form the basis of our classification scheme. After implementing a voting technique for classification, an average classification accuracy of 60% is achieved. Compared to naive classification rate of 33%, we determine that the shapelets are in fact capturing features that are unique in the ERP representation of a unique class.

Olfactory Recognition Based on EEG Gamma-Band Activity

2017 ◽  
Vol 29 (6) ◽  
pp. 1667-1680 ◽  
Author(s):  
Onder Aydemir
Keyword(s):  
Gamma Band ◽  
Eeg Signals ◽  
Accuracy Rate ◽  
Average Classification Accuracy ◽  
Eyes Closed ◽  
Lotus Flower ◽  
Eyes Open ◽  
Gamma Band Activity ◽  
The Brain ◽  
Band Activity

There are various kinds of brain monitoring techniques, including local field potential, near-infrared spectroscopy, magnetic resonance imaging (MRI), positron emission tomography, functional MRI, electroencephalography (EEG), and magnetoencephalography. Among those techniques, EEG is the most widely used one due to its portability, low setup cost, and noninvasiveness. Apart from other advantages, EEG signals also help to evaluate the ability of the smelling organ. In such studies, EEG signals, which are recorded during smelling, are analyzed to determine the subject lacks any smelling ability or to measure the response of the brain. The main idea of this study is to show the emotional difference in EEG signals during perception of valerian, lotus flower, cheese, and rosewater odors by the EEG gamma wave. The proposed method was applied to the EEG signals, which were taken from five healthy subjects in the conditions of eyes open and eyes closed at the Swiss Federal Institute of Technology. In order to represent the signals, we extracted features from the gamma band of the EEG trials by continuous wavelet transform with the selection of Morlet as a wavelet function. Then the [Formula: see text]-nearest neighbor algorithm was implemented as the classifier for recognizing the EEG trials as valerian, lotus flower, cheese, and rosewater. We achieved an average classification accuracy rate of 87.50% with the 4.3 standard deviation value for the subjects in eyes-open condition and an average classification accuracy rate of 94.12% with the 2.9 standard deviation value for the subjects in eyes-closed condition. The results prove that the proposed continuous wavelet transform–based feature extraction method has great potential to classify the EEG signals recorded during smelling of the present odors. It has been also established that gamma-band activity of the brain is highly associated with olfaction.

scholarly journals A New Approach to Detect Epileptic Seizures in Electroencephalograms Using Teager Energy

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Chandrakar Kamath
Keyword(s):  
Epileptic Seizures ◽  
Seizure Detection ◽  
New Approach ◽  
Eyes Closed ◽  
Eyes Open ◽  
Eeg Recordings ◽  
Frequency Changes ◽  
The Time Domain ◽  
Teager Energy ◽  
The Brain

A Teager energy (TE) based approach to discriminate electroencephalogram signals corresponding to nonseizure (eyes open, eyes closed, or interictal) and seizure (ictal) intervals is proposed. Though a good number of contributions have been made for seizure detection, the challenges of unbalanced data (nonseizure and seizure events) and system computational efficiency still remain a challenge. It is reported in the literature that the seizures are characterized by abnormal sudden discharges in the brain which get manifested in the EEG recordings by frequency changes and increased amplitudes. Teager energy (TE) is capable of tracking such rapid changes in frequency as well as amplitude in the time domain. An important finding of this study is that the mean TE quantifier is largely independent of the window length and exhibits relative consistency when used as a relative measure for comparison. We compared the diagnostic capability of TE quantifier with those of Higuchi’s fractal dimension and sample entropy in discriminating nonseizure and seizure states in the EEGs and found that TE outperforms the other two nonlinear quantifiers. The result shows that the application of this method compares favorably with conventional classification methods in terms of performance and is well suited for real-time automatic epileptic seizure detection.

HIGHER ORDER COMPLEXITY OF TIME SERIES

2004 ◽  
Vol 14 (08) ◽  
pp. 2979-2990 ◽  
Author(s):  
FANJI GU ◽  
ENHUA SHEN ◽  
XIN MENG ◽  
YANG CAO ◽  
ZHIJIE CAI
Keyword(s):  
Time Series ◽  
Mental Arithmetic ◽  
Higher Order ◽  
Second Order ◽  
Stationary Time Series ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Logistic Mapping ◽  
Original Time ◽  
Different Order

A concept of higher order complexity is proposed in this letter. If a randomness-finding complexity [Rapp & Schmah, 2000] is taken as the complexity measure, the first-order complexity is suggested to be a measure of randomness of the original time series, while the second-order complexity is a measure of its degree of nonstationarity. A different order is associated with each different aspect of complexity. Using logistic mapping repeatedly, some quasi-stationary time series were constructed, the nonstationarity degree of which could be expected theoretically. The estimation of the second-order complexity of these time series shows that the second-order complexities do reflect the degree of nonstationarity and thus can be considered as its indicator. It is also shown that the second-order complexities of the EEG signals from subjects doing mental arithmetic are significantly higher than those from subjects in deep sleep or resting with eyes closed.

Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

2021 ◽  
pp. 1-11
Author(s):  
Najmeh Pakniyat ◽  
Mohammad Hossein Babini ◽  
Vladimir V. Kulish ◽  
Hamidreza Namazi
Keyword(s):  
Time Series ◽  
Biomedical Science ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Ecg Signals ◽  
Olfactory Stimuli ◽  
Electrocardiogram Ecg ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart’s activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18–22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.

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.

Cognitive Analytics for Rapid Stress Relief in Humans Using EEG Based Analysis of Tratak Sadhana (Meditation)

2020 ◽  
Vol 10 (4) ◽  
pp. 1-20
Author(s):  
Swati Kamthekar ◽  
Prachi Deshpande ◽  
Brijesh Iyer
Keyword(s):  
Big Data ◽  
Fractal Dimensions ◽  
Approximate Entropy ◽  
Stress Relief ◽  
Eeg Signals ◽  
Nonlinear Parameters ◽  
Wavelet Entropy ◽  
Rest State ◽  
Eyes Closed ◽  
The Brain

The article reports the effect of Tratak Sadhana (meditation) on humans using electroencephalograph (EEG) signals. EEG represents the brain activities in the form of electrical signals. Due to non-stationary nature of the EEG signals, nonlinear parameters like approximate entropy, wavelet entropy and Higuchi' fractal dimensions are used to assess the variations in EEG rest as well as during Tratak Sadhana, i.e. at a rest state with eyes closed and during Tratak meditation. EEG signals are captured using EPOC Emotive EEG sensor. The sensor has 14 electrodes covering human scalp. Results shows that new practitioners can also achieve a rapid meditative state as compared to other meditation techniques. Further, the Big Data perspective of the present study is discussed. The present study shows that Tratak Sadhana meditation is an effective tool for rapid stress relief in humans.

scholarly journals Shifts in broadband power and alpha peak frequency observed during long-term isolation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jan Weber ◽  
Timo Klein ◽  
Vera Abeln
Keyword(s):  
Sensory Deprivation ◽  
Peak Frequency ◽  
Neuronal Population ◽  
Confined Space ◽  
Eyes Closed ◽  
Eyes Open ◽  
Eeg Recordings ◽  
Induced Changes ◽  
Alpha Peak

Abstract Prolonged periods of social isolation and spatial confinement do not only represent an issue that needs to be faced by a few astronauts during space missions, but can affect all of us as recently shown during pandemic situations. The fundamental question, how the brain adapts to periods of sensory deprivation and re-adapts to normality, has only received little attention. Here, we use eyes closed and eyes open resting-state electroencephalographic (EEG) recordings to investigate how neural activity is altered during 120 days of isolation in a spatially confined, space-analogue environment. After disentangling oscillatory patterns from 1/f activity, we show that isolation leads to a reduction in broadband power and a flattening of the 1/f spectral slope. Beyond that, we observed a reduction in alpha peak frequency during isolation, but did not find strong evidence for isolation-induced changes that are of oscillatory nature. Critically, all effects reversed upon release from isolation. These findings suggest that isolation and concomitant sensory deprivation lead to an enhanced cortical deactivation which might be explained by a reduction in the mean neuronal population firing rate.

scholarly journals Frequency spectrum recurrence analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Guênia Ladeira ◽  
Norbert Marwan ◽  
João-Batista Destro-Filho ◽  
Camila Davi Ramos ◽  
Gabriela Lima
Keyword(s):  
Time Series ◽  
Frequency Spectrum ◽  
Concentration Level ◽  
Recurrence Analysis ◽  
Similar Frequency ◽  
Eyes Closed ◽  
Analysis Technique ◽  
Recurrence Quantification ◽  
Eyes Open ◽  
Quantification Analysis

AbstractIn this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system.

Sign in / Sign up

Export Citation Format

Share Document