The relationship between the rhythmic components of the brain electrical activity during the development of status epilepticus: an operational model of brain rhythms generation

2021 ◽  
Author(s):  
Alexandr Bondar ◽  
Liubov Shubina
Keyword(s):  
Status Epilepticus ◽  
Electrical Activity ◽  
Brain Electrical Activity ◽  
Operational Model ◽  
Brain Rhythms ◽  
The Relationship ◽  
The Brain

Fit Vs Faint: Assessing Work Causation in “Idiopathic Fall” Cases

2014 ◽  
Vol 19 (5) ◽  
pp. 3-12
Author(s):  
Lorne Direnfeld ◽  
David B. Torrey ◽  
Jim Black ◽  
LuAnn Haley ◽  
Christopher R. Brigham
Keyword(s):  
Blood Flow ◽  
Electrical Activity ◽  
Loss Of Consciousness ◽  
Brain Electrical Activity ◽  
Medical Terminology ◽  
Scientific Analysis ◽  
Medical Causation ◽  
The Individual ◽  
The Brain ◽  
Current Science

Abstract When an individual falls due to a nonwork-related episode of dizziness, hits their head and sustains injury, do workers’ compensation laws consider such injuries to be compensable? Bearing in mind that each state makes its own laws, the answer depends on what caused the loss of consciousness, and the second asks specifically what happened in the fall that caused the injury? The first question speaks to medical causation, which applies scientific analysis to determine the cause of the problem. The second question addresses legal causation: Under what factual circumstances are injuries of this type potentially covered under the law? Much nuance attends this analysis. The authors discuss idiopathic falls, which in this context means “unique to the individual” as opposed to “of unknown cause,” which is the familiar medical terminology. The article presents three detailed case studies that describe falls that had their genesis in episodes of loss of consciousness, followed by analyses by lawyer or judge authors who address the issue of compensability, including three scenarios from Arizona, California, and Pennsylvania. A medical (scientific) analysis must be thorough and must determine the facts regarding the fall and what occurred: Was the fall due to a fit (eg, a seizure with loss of consciousness attributable to anormal brain electrical activity) or a faint (eg, loss of consciousness attributable to a decrease in blood flow to the brain? The evaluator should be able to fully explain the basis for the conclusions, including references to current science.

scholarly journals Identification of Brain Electrical Activity Related to Head Yaw Rotations

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3345
Author(s):  
Enrico Zero ◽  
Chiara Bersani ◽  
Roberto Sacile
Keyword(s):  
Electrical Activity ◽  
Electrical Stimulus ◽  
Head Movements ◽  
Brain Electrical Activity ◽  
Output Function ◽  
Input Output ◽  
Eeg Signals ◽  
Head Turning ◽  
Identification Technique ◽  
The Brain

Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.

Hypoglycemia in Dogs and Recovery After Intracisternal Administration of Glucose

1957 ◽  
Vol 191 (2) ◽  
pp. 367-370 ◽  
Author(s):  
Miles L. Doyle ◽  
Norman S. Olsen
Keyword(s):  
Cerebrospinal Fluid ◽  
Electrical Activity ◽  
Plasma Glucose ◽  
General Circulation ◽  
Severe Hypoglycemia ◽  
Brain Electrical Activity ◽  
Arterial Blood ◽  
Serial Sampling ◽  
The Brain

A standardized procedure for the production of hypoglycemia in dogs has been developed. The degree of hypoglycemia may be determined by the correlation of electroencephalographic tracings and levels of plasma constituents. During severe hypoglycemia glucose was injected either by the intravenous or intracisternal route. In the former group the brain electrical activity returned to normal within 1 minute, whereas, the latter group required 6–10 minutes. Serial sampling of arterial blood in the animals which had been given glucose directly into the cerebrospinal fluid showed that relatively large increases in plasma glucose preceded the return to normal electrical activity of the brain. It is concluded that glucose does not pass directly from the cerebrospinal fluid into the cerebral tissues but rather is transported to the general circulation before entering the brain.

Can a hypercholesterolemic diet change the basal brain electrical activity and during status epilepticus in rats?

2018 ◽  
Vol 34 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Romildo de Albuquerque Nogueira ◽  
Daniella Tavares Pessoa ◽  
Eva Luana Almeida da Silva ◽  
Edbhergue Ventura Lola Costa
Keyword(s):  
Status Epilepticus ◽  
Electrical Activity ◽  
Brain Electrical Activity ◽  
Diet Change ◽  
Hypercholesterolemic Diet ◽  
Basal Brain

scholarly journals Coherence and phase synchronization of the brain electrical activity

2016 ◽  
Vol 20 (4) ◽  
pp. 36-45
Author(s):  
А. О. Попов ◽  
Антон Ваврещук ◽  
А. М. Канайкин
Keyword(s):  
Electrical Activity ◽  
Phase Synchronization ◽  
Brain Electrical Activity ◽  
The Brain

scholarly journals The relationship between the electrical activity of the brain and the development of cerebral infarction

Nosotchu ◽  
1981 ◽  
Vol 3 (3) ◽  
pp. 293-301
Author(s):  
Satoru Tanaka ◽  
Takashi Yoshimoto ◽  
Jiro Suzuki ◽  
Tetsuya Sakamoto
Keyword(s):  
Cerebral Infarction ◽  
Electrical Activity ◽  
The Relationship ◽  
The Brain

scholarly journals A PRELIMINARY STUDY OF BIOLOGICAL ATTRIBUTED RISK FACTOR FOR ANXIETY AMONG ANXIOUS CHILDREN

2019 ◽  
Vol 4 (33) ◽  
pp. 209-222
Author(s):  
Chen Cheong Chen ◽  
Asmidawati Ashari ◽  
Rahimah Ibrahim ◽  
Wan Aliaa W. Sulaiman ◽  
Kian Yong Koo
Keyword(s):  
Risk Factors ◽  
Electrical Activity ◽  
Trait Anxiety ◽  
Significant Relationship ◽  
Anxiety Level ◽  
Anxiety Score ◽  
Brain Electrical Activity ◽  
Biological Risk ◽  
The Brain ◽  
Trait Anxiety Score

Anxiety disorders are chronic, disabling conditions that are distributed across the globe. Woefully, the consistent increase of prevalence rate had affected people across nations range from children to adults. Biological attributed risk factors had associated strongly with the early onset of anxiety during the childhood stage. This current research intended to study the biological risk factors of brain electrical activity, hereditary and gender effect on trait anxiety among anxious children. A total of 36 children, aged ranged from 8 to 13 years old with high trait anxiety level were recruited by using a purposive sampling method. Self- administered STAIC-T and STAIT were used to measure the trait anxiety level of children and parents respectively. Besides, neuroimaging of Quantitative Electroencephalogram (qEEG) brain mapping was administered to study the brain electrical activity and associated brain locations. Pearson’s Correlation was carried out in order to study the relationship between biological risk factors with trait anxiety level. Results showed that there is a significant relationship between parents’ trait anxiety score and children’s trait anxiety score. Preliminary findings indicated that the brain locations of Fp1, F4, F8, T3, and T4 showed a significant relationship with trait anxiety. In conclusion, hereditary and associated brain locations played a role in affecting the trait anxiety level of children and results in the biological vulnerability of anxiety since birth.

scholarly journals EEG Based Headband for Emotion Detection

2019 ◽  
Vol 9 (2) ◽  
pp. 3287-3290
Keyword(s):  
Electrical Activity ◽  
Affective Computing ◽  
Low Noise ◽  
Brain Electrical Activity ◽  
Human Beings ◽  
Emotion Detection ◽  
Eeg Signals ◽  
Peripheral Device ◽  
Low Amplitude ◽  
The Brain

EEG is the term used for recording the brain electrical activity. In Electroencephalography, the encephalon means brain. EEG measures electrical activity generated by thousands of neurons that exists in human brain. The brain electrical activity is measured in voltages. This paper is focused on recognizing emotion from human activity, measured by EEG signals. Making the computer more empathic to the user is one of the aspects of affective computing. With EEG-based emotion detection, the computer can actually take a look inside user’s head to observe their mental state. A low power, low noise and high sensitive analog signal from brain decoded into filtered digital output. The decoder picks a low amplitude and a microvolt signal from brain and decodes it into a filtered and amplified output. As of thelatestattentiongiving fromexaminationteam in creatingsensitivecommunicationamong human beings and peripheral device, the proof of identity of emotive state of the previousdeveloped a necessity. Electro-encephalography establishedimportantconsideration from scientists, becausethey establish modest, inexpensive, transportable, and easily solving theidentification of mind states in this paper.[2] In this paper, it provide a comprehensive overviewfrompresent works in emotion detection using EEG signals

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