Parametric bispectral estimation of EEG signals in different functional states of brain

2000 ◽  
Author(s):  
Shen Minfen
Keyword(s):  
Eeg Signals ◽  
Bispectral Estimation ◽  
Functional States

Parametric Bispectral Estimation of EEG Signals in Disorders of Consciousness

2019 ◽  
Author(s):  
Xile Wei ◽  
Wenli Wang ◽  
Lihui Cai ◽  
Meili Lu ◽  
Guosheng Yi ◽  
...  
Keyword(s):  
Disorders Of Consciousness ◽  
Eeg Signals ◽  
Bispectral Estimation

Microchemical imaging in biomedical research

1992 ◽  
Vol 50 (2) ◽  
pp. 1118-1119
Author(s):  
P. Ingram
Keyword(s):  
Data Analysis ◽  
Electron Probe ◽  
The Other ◽  
X Ray ◽  
Cell Element ◽  
Physiological Information ◽  
Functional States ◽  
Elemental Maps ◽  
Electron Energy Loss ◽  
Secondary Ion

It is well established that unique physiological information can be obtained by rapidly freezing cells in various functional states and analyzing the cell element content and distribution by electron probe x-ray microanalysis. (The other techniques of microanalysis that are amenable to imaging, such as electron energy loss spectroscopy, secondary ion mass spectroscopy, particle induced x-ray emission etc., are not addressed in this tutorial.) However, the usual processes of data acquisition are labor intensive and lengthy, requiring that x-ray counts be collected from individually selected regions of each cell in question and that data analysis be performed subsequent to data collection. A judicious combination of quantitative elemental maps and static raster probes adds not only an additional overall perception of what is occurring during a particular biological manipulation or event, but substantially increases data productivity. Recent advances in microcomputer instrumentation and software have made readily feasible the acquisition and processing of digital quantitative x-ray maps of one to several cells.

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.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

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.

Modeling of EEG Signals Using Wavelets with Iterative Soft Thresholding

2011 ◽  
Vol 6 (4) ◽  
pp. 37-42
Author(s):  
B.krishna Kumar ◽  
◽  
K.V.S.V.R. Prasad ◽  
K. Kishan Rao ◽  
J. Sheshagiri Babu ◽  
...  
Keyword(s):  
Eeg Signals ◽  
Soft Thresholding ◽  
Iterative Soft Thresholding
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