Deep-layer motif method for estimating information flow between EEG signals

Author(s):  
Denggui Fan ◽  
Hui Wang ◽  
Jun Wang
Author(s):  
Tahir Ahmad ◽  
Vinod Ramachandran

The mathematical modelling of EEG signals provides valuable data to neurologists, and is heavily utilized in the diagnosis and treatment of epilepsy. The erratic nature of these signals, coupled with their lack of a consistent visible trend results in a high degree of difficulty in forming a statistical model to describe seizures. Working with Delia-normalized signals, the authors compute the associated Shannon entropies for three sets of data, and show via construction that the information flow during an epileptic seizure can be viewed as a type-2 fuzzy graph.


2019 ◽  
Author(s):  
Dionissios Hristopulos ◽  
Arif Babul ◽  
Shazia Babul ◽  
Leyla R Brucar ◽  
Naznin Virji-Babul

Quantifying the brain's effective connectivity offers a unique window onto the causal architecture coupling the different regions of the brain. Here, we advocate a new, data-driven measure of directed (or effective) brain connectivity based on the recently developed information flow rate coefficient. The concept of the information flow rate is founded in the theory of stochastic dynamical systems and its derivation is based on first principles; unlike various commonly used linear and nonlinear correlations and empirical directional coefficients, the information flow rate can measure causal relations between time series with minimal assumptions. We apply the information flow rate to electroencephalography (EEG) signals in adolescent males to map out the directed, causal, spatial interactions between brain regions during resting-state conditions. To our knowledge, this is the first study of effective connectivity in the adolescent brain. Our analysis reveals that adolescents show a pattern of information flow that is strongly left lateralized, and consists of short and medium ranged bidirectional interactions across the frontal-central-temporal regions. These results suggest an intermediate state of brain maturation in adolescence.


2014 ◽  
Vol 61 (3) ◽  
pp. 680-693 ◽  
Author(s):  
Amir Omidvarnia ◽  
Ghasem Azemi ◽  
Boualem Boashash ◽  
John M. O'Toole ◽  
Paul B. Colditz ◽  
...  

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien

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.


Author(s):  
Selma Büyükgöze

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.


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