scholarly journals Eeg-analysis of the effect of combined use of carbamazepin and mexidolum on the epileptiform brain activity

1997 ◽  
Vol XXIX (1-2) ◽  
pp. 22-26
Author(s):  
О. L. Badalyan ◽  
L. N. Nerobkova ◽  
Т. А. Voronina ◽  
G. N. Avakyan
Keyword(s):  
Brain Activity ◽  
Eeg Analysis ◽  
System Effect ◽  
Anticonvulsive Effect ◽  
Combined Use ◽  
Effective Doses ◽  
Therapeutic Doses ◽  
Anticonvulsive Agents ◽  
Epileptic System

Experiments on rats with chronic epileptogenic fod in sensomotor region of cortex showed that effects of anticonvulsive agents depend on the progression phase of epileptic system. Effect of carbamasepinum in effective doses is mostly expressed during the phase of stable epileptic system with determinant foci in limbico-hypothalamic structures. Lowering the doses of the drug brings about a decrease of anticonvulsive effect and activation of the secondary hippocampal focus. Combined use of carbamazepin and mexidolum with membra-notropic antioxidantal activity bbrings about an increase of anticonvulsive properties of carbamazepin and allows to decrease the therapeutic doses of the last.

scholarly journals Daytime micro-naps in a nocturnal migrant: an EEG analysis

2008 ◽  
Vol 5 (1) ◽  
pp. 77-80 ◽  
Author(s):  
T Fuchs ◽  
D Maury ◽  
F.R Moore ◽  
V.P Bingman
Keyword(s):  
Brain Activity ◽  
Sleep Loss ◽  
Eeg Analysis ◽  
Observable Effect ◽  
Brain Response ◽  
Night Time ◽  
Avian Migration ◽  
Unihemispheric Sleep ◽  
Daytime Behaviour ◽  
Behavioural Evidence

Many species of typically diurnal songbirds experience sleep loss during the migratory seasons owing to their nocturnal migrations. However, despite substantial loss of sleep, nocturnally migrating songbirds continue to function normally with no observable effect on their behaviour. It is unclear if and how avian migrants compensate for sleep loss. Recent behavioural evidence suggests that some species may compensate for lost night-time sleep with short, uni- and bilateral ‘micro-naps’ during the day. We provide electrophysiological evidence that short episodes of sleep-like daytime behaviour (approx. 12 s) are accompanied by sleep-like changes in brain activity in an avian migrant. Furthermore, we present evidence that part of this physiological brain response manifests itself as unihemispheric sleep, a state during which one brain hemisphere is asleep while the other hemisphere remains essentially awake. Episodes of daytime sleep may represent a potent adaptation to the challenges of avian migration and offer a plausible explanation for the resilience to sleep loss in nocturnal migrants.

Influence of dental magnets on human brain activity: A quantitative EEG analysis

2021 ◽  
Author(s):  
Angel Elias ◽  
Fathima Banu Raza ◽  
Anand Kumar Vaidyanathan ◽  
Padmanabhan Thallam Veeravalli
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
Quantitative Eeg ◽  
Eeg Analysis ◽  
Quantitative Eeg Analysis

scholarly journals Special Issue on Brain Machine/Computer Interface and its Application

2020 ◽  
Vol 32 (4) ◽  
pp. 723-723
Author(s):  
Shoichiro Fujisawa ◽  
Minoru Fukumi ◽  
Jianting Cao ◽  
Yasue Mitsukura ◽  
Shin-ichi Ito
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Eeg Analysis ◽  
Special Issue ◽  
Eeg Signals ◽  
Auditory P300 ◽  
Environment Control ◽  
Electroencephalogram Eeg ◽  
Human Support

Brain machine/computer interface (BMI/BCI) technologies are based on analyzing brain activity to control machines and support the communication of commands and messages. To sense brain activities, a functional NIRS and electroencephalogram (EEG) that has been developed for that purpose is often employed. Analysis techniques and algorithms for the NIRS and EEG signals have also been created, and human support systems in the form of BMI/BCI applications have been developed. In the field of rehabilitation, BMI/BCI is used to control environment control systems and electric wheelchairs. In medicine, BMI/BCI is used to assist in communications for patient support. In industry, BMI/BCI is used to analyze sensibility and develop novel games. This special issue on Brain Machine/Computer Interface and its Application includes six interesting papers that cover the following topics: an EEG analysis method for human-wants detection, cognitive function using EEG analysis, auditory P300 detection, a wheelchair control BCI using SSVEP, a drone control BMI based on SSVEP that uses deep learning, and an improved CMAC model. We thank all authors and reviewers of the papers and the Editorial Board of Journal of Robotics and Mechatronics for its help with this special issue.

scholarly journals Pitfalls in EEG analysis in patients with non-convulsive status epilepticus

2020 ◽  
Author(s):  
Ying Wang ◽  
Ivan C Zibrandtsen ◽  
Richard HC Lazeron ◽  
Johannes P van Dijk ◽  
Xi Long ◽  
...  
Keyword(s):  
Status Epilepticus ◽  
Brain Activity ◽  
Automated Analysis ◽  
Kappa Statistics ◽  
Convulsive Status Epilepticus ◽  
Eeg Analysis ◽  
Gradual Onset ◽  
Movement Artifacts ◽  
Eeg Recordings ◽  
Interictal Discharges

AbstractObjectiveElectroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis are still not reliable for the diagnosis of non-convulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided.MethodsWe analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) were visually analyzed by two independent raters. We investigated whether unreliable EEG visual interpretations quantified by low inter-rater agreement can be predicted by the characteristics of ictal discharges and individuals’ clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, two epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis.ResultsShort ictal discharges with a gradual onset (developing over 3 seconds in length) were liable to be misinterpreted. An extra 2 minutes of ictal discharges contributed to an increase in the kappa statistics of > 0.1. Other problems were the misinterpretation of abnormal background activity (slow wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges.ConclusionA longer duration criterion for NCSE-EEGs than 10 seconds that commonly used in NCSE working criteria is needed. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.

Long-Term Treatment of Depression

1994 ◽  
Vol 165 (S26) ◽  
pp. 31-36 ◽  
Author(s):  
Stuart A. Montgomery
Keyword(s):  
Acute Treatment ◽  
Selective Serotonin Reuptake Inhibitors ◽  
Term Treatment ◽  
Recurrent Depression ◽  
Recurrence Prevention ◽  
Treatment Of Depression ◽  
Long Term Treatment ◽  
Effective Doses ◽  
Therapeutic Doses

Long-term treatment of depression encompasses two separate phases: relapse and recurrence prevention. Relapse prevention aims to consolidate the response to acute treatment. Some tricyclic antidepressants (TCAs) have been shown to be effective, possibly in lower than standard acute treatment doses. The selective serotonin reuptake inhibitors (SSRIs) have been shown to be effective at the same minimum effective doses used to treat acute depression, or in a lower dose as with citalopram. Recurrence prevention aims to reduce the risk of onset of a new episode of depression in patients with recurrent depression. Imipramine has been thoroughly studied in unipolar depressed patients in full therapeutic doses for up to five years and is clearly effective. Other TCAs have not been adequately tested and may not all be equally effective. The SSRIs fluoxetine, paroxetine and sertraline have also been shown to be effective in reducing the risk of new episodes of depression.

Pitfalls in EEG Analysis in Patients With Nonconvulsive Status Epilepticus: A Preliminary Study

2021 ◽  
pp. 155005942110504
Author(s):  
Ying Wang ◽  
Ivan C. Zibrandtsen ◽  
Richard H. C. Lazeron ◽  
Johannes P. van Dijk ◽  
Xi Long ◽  
...  
Keyword(s):  
Status Epilepticus ◽  
Brain Activity ◽  
Automated Analysis ◽  
Nonconvulsive Status Epilepticus ◽  
Kappa Statistics ◽  
Eeg Analysis ◽  
Gradual Onset ◽  
Movement Artifacts ◽  
Eeg Recordings ◽  
Interictal Discharges

Objective: Electroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis were still not reliable for the diagnosis of nonconvulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided. Methods: We analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) was visually analyzed by 2 independent raters. We investigated whether unreliable EEG visual interpretations quantified by low interrater agreement can be predicted by the characteristics of ictal discharges and individuals’ clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, 2 epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis. Results: Short ictal discharges with a gradual onset (developing over 3 s in length) were liable to be misinterpreted. An extra 2 min of ictal discharges contributed to an increase in the kappa statistics of >0.1. Other problems were the misinterpretation of abnormal background activity (slow-wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges. Conclusion: A longer duration criterion for NCSE-EEGs than 10 s that is commonly used in NCSE working criteria is recommended. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.

Application of EEG Analysis in Cognitive Science

2014 ◽  
Vol 519-520 ◽  
pp. 816-819
Author(s):  
Lin Liu ◽  
Guo Zhuang Liang
Keyword(s):  
Cognitive Science ◽  
Time Resolution ◽  
Brain Activity ◽  
Eeg Analysis

EEG analysis is an effective noninvasive mean to understand the mechanism of brain activity.Recently with the increasing of research for cognitive science,EEG analysis becomes one of the most important the methods in the field of cognitive research.This paper reviews methods of sign al processing on EEG an d the progress of EEG analysis for cognitive science on the basis of the introduction for cognitive research.The paper states that it is significative to study EEG deeply for understanding the process of cognitive and thinking,unveiling the mechanism of brain activity because EEG has good characteristics such as time resolution.

Visualization of EEG Using Time-Frequency Distributions

1997 ◽  
Vol 36 (04/05) ◽  
pp. 298-301 ◽  
Author(s):  
B. Stiber ◽  
S. Sato
Keyword(s):  
Brain Activity ◽  
Temporal Structure ◽  
Frequency Resolution ◽  
Signal Frequency ◽  
Time Varying ◽  
Eeg Analysis ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Temporal Features ◽  
Time Varying Frequency

Abstract:The EEG is a time-varying or nonstationary signal. Frequency and amplitude are two of its significant characteristics, and are valuable clues to different states of brain activity. Detection of these temporal features is important in understanding EEGs. Commonly, spectrograms and AR models are used for EEG analysis. However, their accuracy is limited by their inherent assumption of stationarity and their trade-off between time and frequency resolution. We investigate EEG signal processing using existing compound kernel time-frequency distributions (TFDs). By providing a joint distribution of signal intensity at any frequency along time, TFDs preserve details of the temporal structure of the EEG waveform, and can extract its time-varying frequency and amplitude features. We expect that this will have significant implications for EEG analysis and medical diagnosis.

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