A method of clinical EEG signals analysis based on the symbolic entropy

2014 ◽  
Author(s):  
Minfen Shen ◽  
Jiachun Weng
Keyword(s):  
Eeg Signals ◽  
Clinical Eeg

An Approach for Predicting Clinical EEG Signal Based on SVM

2013 ◽  
Vol 411-414 ◽  
pp. 1134-1138
Author(s):  
Min Fen Shen ◽  
Jia Chun Weng
Keyword(s):  
Brain Diseases ◽  
Risk Minimization ◽  
Eeg Signals ◽  
Local Method ◽  
Dynamical Characteristics ◽  
Proposed Model ◽  
Long Time ◽  
Bottle Neck ◽  
Learning Machine ◽  
Clinical Eeg

Accurate modeling of Electroencephalography (EEG) signals is an important problem in clinical diagnosis of brain diseases. The method using support vectors machine (SVM) based on the structure risk minimization provides us an effective way of learning machine. But solving the quadratic programming problem for training SVM becomes a bottle-neck of using SVM because of the long time of SVM training. In this paper, a local-SVM method is proposed for modeling EEG signals. The local method is presented for improving the speed of the prediction of EEG signals. Furthermore, this proposed model is used to detect epilepsy from EEG signals in which dynamical characteristics are difference between normal and epilepsy EEG signals. The experimental results show that the training of the local-SVM obtains a good behavior. In addition, the local SVM method significantly improves the prediction and detection precision.

scholarly journals Filter Band Multicarrier Based Transmission Technology for Clinical EEG Signals

2017 ◽  
Author(s):  
Chin-Feng Lin ◽  
Wei-Syuan Chao ◽  
Jun-Da Chen
Keyword(s):  
Eeg Signals ◽  
Clinical Eeg

scholarly journals Uncovering the structure of clinical EEG signals with self-supervised learning

2020 ◽  
Author(s):  
Hubert Banville ◽  
Omar Chehab ◽  
Aapo Hyvarinen ◽  
Denis Engemann ◽  
Alexandre Gramfort
Keyword(s):  
Supervised Learning ◽  
Eeg Signals ◽  
Clinical Eeg

A chaos-based visual encryption mechanism for clinical EEG signals

2009 ◽  
Vol 47 (7) ◽  
pp. 757-762 ◽  
Author(s):  
Chin-Feng Lin ◽  
Cheng-Hsing Chung ◽  
Jia-Hui Lin
Keyword(s):  
Eeg Signals ◽  
Clinical Eeg

Full-Band EEG (FbEEG): A New Standard for Clinical Electroencephalography

2005 ◽  
Vol 36 (4) ◽  
pp. 311-317 ◽  
Author(s):  
Sampsa Vanhatalo ◽  
Juha Voipio ◽  
Kai Kaila
Keyword(s):  
Frequency Band ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Adult Brain ◽  
Eeg Signals ◽  
Trade Offs ◽  
Positron Emission ◽  
Full Band ◽  
Clinical Eeg ◽  
Neuroimaging Techniques

A variety of neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and magnetoencephalography (MEG), have been established during the last few decades, with progressive improvements continuously taking place in the underlying technologies. In contrast to this, the recording bandwidth of the routine clinical EEG (typically around 0.5–50 Hz) that was originally set by trivial technical limitations has remained practically unaltered for over half a decade. An increasing amount of evidence shows that salient EEG signals take place and can be recorded beyond the conventional clinical EEG bandwidth. These physiological and pathological EEG activities range from 0.01 Hz to several hundred Hz, and they have been demonstrated in recordings of spontaneous activity in the preterm human brain, and during epileptic seizures, sleep, as well as in various kinds of cognitive tasks and states in the adult brain. In the present paper, we will describe the practical aspects of recording the full physiological frequency band of the EEG (Full-band EEG; FbEEG), and we review the currently available data on the clinical applications of FbEEG. Recording the FbEEG is readily attained with commercially available direct-current (DC) coupled amplifiers if the recording setup includes electrodes providing a DC-stable electrode-skin interface. FbEEG does not have trade-offs that would favor any frequency band at the expense of another. We present several arguments showing that elimination of the lower ( infraslow) or higher ( ultrafast) bands of the EEG frequency spectrum in routine EEG has led, and will lead, to situations where salient and physiologically meaningful features of brain activity remain undetected or become seriously attenuated and distorted. With the currently available electrode, amplifier and data acquisition technology, it is to be expected that FbEEG will become the standard approach in both clinical and basic science.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

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