The effect of Neurofeedback training on brain wave activity and cognitive performance in chronic stroke patients

2013 ◽  
Vol 14 (5) ◽  
pp. 2329-2337 ◽  
Author(s):  
Young-Sin Lee ◽  
Sang-Yeob Kim ◽  
Chan-Kyu Kim ◽  
Dae-In Jung ◽  
Kyung-Yoon Kim
Keyword(s):  
Cognitive Performance ◽  
Wave Activity ◽  
Chronic Stroke ◽  
Neurofeedback Training ◽  
Stroke Patients ◽  
Brain Wave ◽  
Brain Wave Activity

The use of propofol as an induction agent for halothane and isoflurane anesthesia in dogs

1998 ◽  
Vol 34 (1) ◽  
pp. 84-91 ◽  
Author(s):  
A Bufalari ◽  
SM Miller ◽  
C Giannoni ◽  
CE Short
Keyword(s):  
Blood Pressure ◽  
Wave Activity ◽  
Simultaneous Increase ◽  
Induction Agent ◽  
Total Amplitude ◽  
Brain Wave ◽  
Isoflurane Anesthesia ◽  
Arterial Blood ◽  
Anesthetized Dogs ◽  
Brain Wave Activity

Cardiovascular, pulmonary, and quantitative electroencephalographic parameters were assessed in 12 anesthetized dogs to determine the compatibility of the injectable anesthetic propofol with halothane and isoflurane. No cases of apnea were observed during induction of anesthesia. An adequate level of anesthesia was established in each protocol as judged by both the lack of response to mechanical noxious stimuli (i.e., tail clamping) and evidence of reduction in total amplitude of brain wave activity. The initial propofol-mediated decrease in arterial blood pressure continued during either halothane (52.4%) or isoflurane (38%) anesthesia without a simultaneous increase in heart rate. The results of this study suggest that propofol, in combination with inhalant agents, can be used effectively and safely for canine anesthesia in veterinary practice.

ALTERED BRAIN WAVE ACTIVITY IN PERSONS WITH CHRONIC SPINAL CORD INJURY

2007 ◽  
Vol 117 (12) ◽  
pp. 1731-1746 ◽  
Author(s):  
D. HERBERT ◽  
Y. TRAN ◽  
A. CRAIG ◽  
P. BOORD ◽  
J. MIDDLETON ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Wave Activity ◽  
Chronic Spinal Cord Injury ◽  
Brain Wave ◽  
Cord Injury ◽  
Brain Wave Activity

Regional brain wave activity changes associated with fatigue

2012 ◽  
Vol 49 (4) ◽  
pp. 574-582 ◽  
Author(s):  
Ashley Craig ◽  
Yvonne Tran ◽  
Nirupama Wijesuriya ◽  
Hung Nguyen
Keyword(s):  
Wave Activity ◽  
Brain Wave ◽  
Regional Brain ◽  
Brain Wave Activity

COGNITIVE ACTIVITY IN THE RESPECT OF QEEG RESEARCH - PRESENTATION OF LABORATORY TESTS

2019 ◽  
Vol 5 ◽  
pp. 469
Author(s):  
Tomasz Prauzner ◽  
Małgorzata Prauzner ◽  
Kacper Prauzner ◽  
Paweł Ptak
Keyword(s):  
Computer Program ◽  
Experimental Research ◽  
Laboratory Tests ◽  
Brain Activity ◽  
Cognitive Activity ◽  
Wave Activity ◽  
Program Visualization ◽  
Brain Wave ◽  
Research Results ◽  
Brain Wave Activity

The article presents the methodology of laboratory tests carried out in the Laboratory of Experimental Research Biofeedback of the Jan Dlugosz University in Czestochowa (Poland) regarding the evaluation of education effectiveness by registering brain wave activity using electroencephalographic research (EEG method). The research results indicate that, depending on the form of the computer program visualization, a change in the activity of SMR, Beta1, Beta2 and Gamma waves was observed. The results are presented in the form of graphs and 2D brain activity images using the equipment Mitsar EEG 202 and WinEEG software

scholarly journals Deep Learning on Game Addiction Detection Based on Electroencephalogram

2021 ◽  
Vol 5 (3) ◽  
pp. 963
Author(s):  
Lalu Arfi Maulana Pangistu ◽  
Ahmad Azhari
Keyword(s):  
Brain Activity ◽  
Late Adolescence ◽  
Wave Activity ◽  
Test Results ◽  
Brain Wave ◽  
Brain Waves ◽  
Game Addiction ◽  
Brain Wave Activity ◽  
Electroencephalogram Eeg ◽  
The Brain

Playing games for too long can be addictive. Based on a recent study by Brand et al, adolescents are considered more vulnerable than adults to game addiction. The activity of playing games produces a wave in the brain, namely beta waves where the person is in a focused state. Brain wave activity can be measured and captured using an Electroencephalogram (EEG). Recording brain wave activity naturally requires a prominent and constant brain activity such as when concentrating while playing a game. This study aims to detect game addiction in late adolescence by applying Convolutional Neural Network (CNN). Recording of brain waves was carried out three times for each respondent with a stimulus to play three different games, namely games included in the easy, medium, and hard categories with a consecutive taking time of 10 minutes, 15 minutes, and 30 minutes. Data acquisition results are feature extraction using Fast Fourier Transform to get the average signal for each respondent. Based on the research conducted, obtained an accuracy of 86% with a loss of 0.2771 where the smaller the loss value, the better the CNN model built. The test results on the model produce an overall accuracy of 88% with misclassification in 1 data. The CNN model built is good enough for the detection of game addiction in late adolescence. 

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