scholarly journals Detection of Static, Dynamic, and No Tactile Friction Based on Non-linear dynamics of EEG Signals: A Preliminary Study

2020 ◽  
Author(s):  
Golnaz Baghdadi ◽  
Mahmood Amiri
Keyword(s):  
Lyapunov Exponent ◽  
Static Friction ◽  
Good Choice ◽  
Dynamic Friction ◽  
Eeg Signals ◽  
Linear Features ◽  
Linear Dynamics ◽  
Non Linear ◽  
Tactile Friction ◽  
Tactile System

ABSTRACTTouching an object leads to a frictional interaction between the skin and the object. There are two kinds of friction: the first contact that leads to static friction and the dragging phase that leads to dynamic friction. No study has been performed to show the effect of friction type on EEG signals. The main goal of the current study is to investigate the effect of tactile friction on non-linear features of EEG signals.Participants performed a tactile task that each of its trials had three states: the sensation of 1) static friction, 2) dynamic friction, and 3) no friction. During the experiment, EEG signals were recorded, and different linear and non-linear EEG indices were extracted and analyzed to find the effect of the tactile friction on EEG signals.Linear features such as spectral features were not a good choice to distinguish between the states. However, non-linear features such as Lyapunov exponent, Higuchi’s dimension, and Hurst exponent had the potential to separate the mentioned states. Results also showed signs of predictability (negative Lyapunov exponent) in the signals recorded during dynamic friction and the existence of long-range dependency (memory) in EEG signals recorded during all states. The complexity of the tactile system in Theta band was also higher than the Delta band. The results of this research not only increase our knowledge about brain non-linear dynamics in response to tactile friction but also lead to a design of a preliminary system that can automatically detect friction between the skin and surfaces.

scholarly journals EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: [email protected]) [PDF] Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press)

2012 ◽  
pp. 326-330 ◽  
Author(s):  
S. M. Shafiul Alam ◽  
S. M. Shafiul Alam ◽  
Aurangozeb ◽  
Syed TarekShahriar
Keyword(s):  
Correlation Dimension ◽  
Largest Lyapunov Exponent ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Linear Dynamics ◽  
Signal Discrimination ◽  
Non Linear ◽  
Electroencephalogram Eeg ◽  
Seizure Attacks ◽  
Healthy Persons

ALCOHOLIC INDEX USING NON-LINEAR FEATURES EXTRACTED FROM DIFFERENT FREQUENCY BANDS

2017 ◽  
Vol 17 (07) ◽  
pp. 1740009
Author(s):  
G. MURALIDHAR BAIRY ◽  
U. C. NIRANJAN ◽  
SHU LIH OH ◽  
JOEL E. W. KOH ◽  
VIDYA K. SUDARSHAN ◽  
...  
Keyword(s):  
Fuzzy Entropy ◽  
Multiscale Entropy ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Linear Features ◽  
Entropy Formula ◽  
Non Linear ◽  
The Brain

Alcoholism is a complex condition that mainly disturbs the neuronal networks in Central Nervous System (CNS). This disorder not only disturbs the brain, but also affects the behavior, emotions, and cognitive judgements. Electroencephalography (EEG) is a valuable tool to examine the neuropsychiatric disorders like alcoholism. The EEG is a well-established modality to diagnose the electrical activity produced by the populations of neurons in cerebral cortex. However, EEG signals are non-linear in nature; hence very challenging to interpret the valuable information from them using linear methods. Thus, using non-linear methods to analyze EEG signals can be beneficial in order to predict the brain signals condition. This paper presents a computer-aided diagnostic method for the detection of alcoholic EEG signals from normal by employing the non-linear techniques. First, the EEG signals are subjected to six levels of Wavelet Packet Decomposition (WPD) to obtain seven wavebands (delta ([Formula: see text]), theta ([Formula: see text]), lower alpha (la), upper alpha (ua), lower beta (lb), upper beta (ub), lower gamma (lg)). From each wavebands (activity bands), 19 non-linear features such as Recurrence Quantification Analysis (RQA) ([Formula: see text]), Approximate Entropy ([Formula: see text]), Energy ([Formula: see text]), Fractal Dimension (FD) ([Formula: see text]), Permutation Entropy ([Formula: see text]), Detrended Fluctuation Analysis ([Formula: see text]), Hurst Exponent ([Formula: see text]), Largest Lyapunov Exponent ([Formula: see text]), Sample Entropy ([Formula: see text]), Shannon’s Entropy ([Formula: see text]), Renyi’s entropy ([Formula: see text]), Tsalli’s entropy ([Formula: see text]), Fuzzy entropy ([Formula: see text]), Wavelet entropy ([Formula: see text]), Kolmogorov–Sinai entropy ([Formula: see text]), Modified Multiscale Entropy ([Formula: see text]), Hjorth’s parameters (activity ([Formula: see text]), mobility ([Formula: see text]), and complexity ([Formula: see text])) are extracted. The extracted features are then ranked using Bhattacharyya, Entropy, Fuzzy entropy-based Max-Relevancy and Min-Redundancy (mRMR), Receiver Operating Characteristic (ROC), [Formula: see text]-test, and Wilcoxon. These ranked features are given to train Support Vector Machine (SVM) classifier. The SVM classifier with radial basis function (RBF) achieved 95.41% accuracy, 93.33% sensitivity and 97.50% specificity using four non-linear features ranked by Wilcoxon method. In addition, an integrated index called Alcoholic Index (ALCOHOLI) is developed using highly ranked two features for identification of normal and alcoholic EEG signals using a single number. This system is rapid, efficient, and inexpensive and can be employed as an EEG analysis assisting system by clinicians in the detection of alcoholism. In addition, the proposed system can be used in rehabilitation centers to evaluate person with alcoholism over time and observe the outcome of treatment provided for reducing or reversing the impact of the condition on the brain.

APPLICATION OF NON-LINEAR AND WAVELET BASED FEATURES FOR THE AUTOMATED IDENTIFICATION OF EPILEPTIC EEG SIGNALS

2012 ◽  
Vol 22 (02) ◽  
pp. 1250002 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
S. VINITHA SREE ◽  
PENG CHUAN ALVIN ANG ◽  
RATNA YANTI ◽  
JASJIT S. SURI
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Automatic Monitoring ◽  
Gaussian Mixture ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Fuzzy Classifier ◽  
Linear Features ◽  
Non Linear

Epilepsy, a neurological disorder, is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are non-linear and dynamic in nature. Visual inspection of the EEG signals for detection of normal, interictal, and ictal activities is a strenuous and time-consuming task due to the huge volumes of EEG segments that have to be studied. Therefore, non-linear methods are being widely used to study EEG signals for the automatic monitoring of epileptic activities. The aim of our work is to develop a Computer Aided Diagnostic (CAD) technique with minimal pre-processing steps that can classify all the three classes of EEG segments, namely normal, interictal, and ictal, using a small number of highly discriminating non-linear features in simple classifiers. To evaluate the technique, segments of normal, interictal, and ictal EEG segments (100 segments in each class) were used. Non-linear features based on the Higher Order Spectra (HOS), two entropies, namely the Approximation Entropy (ApEn) and the Sample Entropy (SampEn), and Fractal Dimension and Hurst Exponent were extracted from the segments. Significant features were selected using the ANOVA test. After evaluating the performance of six classifiers (Decision Tree, Fuzzy Sugeno Classifier, Gaussian Mixture Model, K-Nearest Neighbor, Support Vector Machine, and Radial Basis Probabilistic Neural Network) using a combination of the selected features, we found that using a set of all the selected six features in the Fuzzy classifier resulted in 99.7% classification accuracy. We have demonstrated that our technique is capable of achieving high accuracy using a small number of features that accurately capture the subtle differences in the three different types of EEG (normal, interictal, and ictal) segments. The technique can be easily written as a software application and used by medical professionals without any extensive training and cost. Such software can evolve into an automatic seizure monitoring application in the near future and can aid the doctors in providing better and timely care for the patients suffering from epilepsy.

Low pressure hydrocephalus and ventriculomegaly: hysteresis, non-linear dynamics, and the benefits of CSF diversion

2002 ◽  
Vol 16 (6) ◽  
pp. 555-561 ◽  
Author(s):  
M. S. Lesniak ◽  
R. E. Clatterbuck ◽  
D. Rigamonti ◽  
M. A. Williams
Keyword(s):  
Low Pressure ◽  
Linear Dynamics ◽  
Non Linear ◽  
Csf Diversion

Non-linear dynamics in DIS at NLO

2017 ◽  
Author(s):  
Giovanni Antonio Chirilli
Keyword(s):  
Linear Dynamics ◽  
Non Linear
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