Electroencephalogram activity in the anoxic turtle brain

1997 ◽  
Vol 273 (3) ◽  
pp. R911-R919 ◽  
Author(s):  
J. A. Fernandes ◽  
P. L. Lutz ◽  
A. Tannenbaum ◽  
A. T. Todorov ◽  
L. Liebovitch ◽  
...  
Keyword(s):  
Transition Period ◽  
Activity Level ◽  
Sharp Waves ◽  
Energy Demands ◽  
Order Of Magnitude ◽  
Activity State ◽  
Low Amplitude ◽  
Electroencephalogram Eeg ◽  
Electrical Activities ◽  
The Brain

The anoxia-tolerant turtle brain slowly undergoes a complex sequence of changes in electroencephalogram (EEG) activity as the brain systematically downregulates its energy demands. Following N2 respiration, the root mean square voltage rapidly fell, reaching approximately 20% of normoxic levels after approximately 100 min of anoxia. During the first 20- to 40-min transition period, the power of the EEG decreased substantially, particularly in the 12- to 24-Hz band, with low-amplitude slow wave activity predominating (3-12 Hz). Bursts of high voltage rhythmic slow (approximately 3-8 Hz) waves were seen during the 20- to 100-min period of anoxia, accompanied by large sharp waves. During the next 400 min of N2 respiration, two distinct patterns of electrical activity characterized the anoxic turtle brain: 1) a sustained but depressed activity level, with an EEG amplitude approximately 20% of the normoxic control and with total EEG power reduced by one order of magnitude at all frequencies, and 2) short (3-15 s) periodic (0.5-2/min) bursts of mixed-frequency activity that interrupted the depressed activity state. We speculate that the EEG patterns seen during sustained anoxia represent the minimal or basic electrical activities that are compatible with the survival of the anoxic turtle brain as an integrated unit, which allow the brain to return to normal functioning when air respiration resumed.

scholarly journals Prediction of REM (Rapid Eye Movement) Sleep Behaviour Disorder using EEG Signal applied EMG1 and EMG2 Channel

2021 ◽  
Vol 14 (01) ◽  
pp. 519-524
Author(s):  
Mohd. Maroof Siddiqui ◽  
Ruchin Jain
Keyword(s):  
Sleep Disorder ◽  
Normal Subjects ◽  
Time Frequency ◽  
Brain Signals ◽  
Eeg Data ◽  
Spectrum Density ◽  
Short Time ◽  
Electroencephalogram Eeg ◽  
Electrical Activities ◽  
The Brain

This sleep disorder is reflected as the changes in the electrical activities and chemical activities in the brain that can be observed by capturing the brain signals and the images. In this research, Short Time-frequency analysis of Power Spectrum Density (STFAPSD) approach applied on Electroencephalogram (EEG) Signals for prediction of RBD sleep disorder. Collection of Electroencephalogram (EEG) of normal subjects & different type of sleep disordered subjects & application of signal processing on EEG data for development the algorithm for detection of sleep disorder and implementation in MATLAB.

scholarly journals Cross-Modal Effect of Presenting Visual and Force Feedback That Create the Illusion of Stair-Climbing

2021 ◽  
Vol 11 (7) ◽  
pp. 2987
Author(s):  
Takumi Okumura ◽  
Yuichi Kurita
Keyword(s):  
Visual Information ◽  
Force Feedback ◽  
Activity Level ◽  
Experimental Results ◽  
Stair Climbing ◽  
Mirror Therapy ◽  
Visual Condition ◽  
Stroke Patients ◽  
Head Mount Display ◽  
The Brain

Image therapy, which creates illusions with a mirror and a head mount display, assists movement relearning in stroke patients. Mirror therapy presents the movement of the unaffected limb in a mirror, creating the illusion of movement of the affected limb. As the visual information of images cannot create a fully immersive experience, we propose a cross-modal strategy that supplements the image with sensual information. By interacting with the stimuli received from multiple sensory organs, the brain complements missing senses, and the patient experiences a different sense of motion. Our system generates the sense of stair-climbing in a subject walking on a level floor. The force sensation is presented by a pneumatic gel muscle (PGM). Based on motion analysis in a human lower-limb model and the characteristics of the force exerted by the PGM, we set the appropriate air pressure of the PGM. The effectiveness of the proposed system was evaluated by surface electromyography and a questionnaire. The experimental results showed that by synchronizing the force sensation with visual information, we could match the motor and perceived sensations at the muscle-activity level, enhancing the sense of stair-climbing. The experimental results showed that the visual condition significantly improved the illusion intensity during stair-climbing.

Detection of Intracranial Signatures of Interictal Epileptiform Discharges from Concurrent Scalp EEG

2016 ◽  
Vol 26 (04) ◽  
pp. 1650016 ◽  
Author(s):  
Loukianos Spyrou ◽  
David Martín-Lopez ◽  
Antonio Valentín ◽  
Gonzalo Alarcón ◽  
Saeid Sanei
Keyword(s):  
Detection Algorithm ◽  
The Other ◽  
Intracranial Eeg ◽  
Scalp Eeg ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Scalp Electroencephalogram ◽  
Patients With Epilepsy ◽  
Electrical Activities ◽  
The Brain

Interictal epileptiform discharges (IEDs) are transient neural electrical activities that occur in the brain of patients with epilepsy. A problem with the inspection of IEDs from the scalp electroencephalogram (sEEG) is that for a subset of epileptic patients, there are no visually discernible IEDs on the scalp, rendering the above procedures ineffective, both for detection purposes and algorithm evaluation. On the other hand, intracranially placed electrodes yield a much higher incidence of visible IEDs as compared to concurrent scalp electrodes. In this work, we utilize concurrent scalp and intracranial EEG (iEEG) from a group of temporal lobe epilepsy (TLE) patients with low number of scalp-visible IEDs. The aim is to determine whether by considering the timing information of the IEDs from iEEG, the resulting concurrent sEEG contains enough information for the IEDs to be reliably distinguished from non-IED segments. We develop an automatic detection algorithm which is tested in a leave-subject-out fashion, where each test subject’s detection algorithm is based on the other patients’ data. The algorithm obtained a [Formula: see text] accuracy in recognizing scalp IED from non-IED segments with [Formula: see text] accuracy when trained and tested on the same subject. Also, it was able to identify nonscalp-visible IED events for most patients with a low number of false positive detections. Our results represent a proof of concept that IED information for TLE patients is contained in scalp EEG even if they are not visually identifiable and also that between subject differences in the IED topology and shape are small enough such that a generic algorithm can be used.

CLINICAL AND ELECTROPHYSIOLOGICAL INDICATORS OF ACUTE AND EARLY RECOVERY PERIODS OF CEREBRAL STROKE

Vestnik ◽  
2021 ◽  
pp. 46-50
Author(s):  
Д.А. Митрохин ◽  
М.М. Ибрагимов ◽  
А.Н. Симбинова ◽  
Н.Ш. Буйракулова ◽  
В.В. Харченко ◽  
...  
Keyword(s):  
Practical Interest ◽  
Tension Headache ◽  
Haemorrhagic Stroke ◽  
Speech Disorders ◽  
Cerebral Stroke ◽  
Bioelectrical Activity ◽  
Early Recovery ◽  
Sharp Waves ◽  
Coherence Index ◽  
The Brain

В остром и раннем восстановительном периодах церебрального инсульта взаимосвязь между биоэлектрической активностью головного мозга и клинической картиной заболевания представляют значительный научный и практический интерес. В данной статье, представлены результаты исследования клинико-неврологических и электроэнцефалографических показателей, в остром и раннем восстановительном периодах церебрального инсульта, 67 больных в возрасте от 43 до 78 лет. Показано, что у больных в остром и раннем восстановительном периодах церебрального инсульта на фоне двигательных и речевых расстройств, наблюдались легкие и умеренные когнитивные нарушения, а также тревожно-депрессивные проявления. Головная боль, соответствующая критериям головной боли напряжения отмечалась у 61,1% больных. Биоэлектрическая активность головного мозга характеризовалась выраженной дельта и тета активностью, а также единичными острыми волнами, спайками, преимущественно в пораженном полушарии головного мозга, межполушарной асимметрией, повышением мощности спектров в сторону преобладания медленных волн. Показатели индекса когерентности по всем отведениям были снижены, что свидетельствует о нарушении функциональных межполушарных взаимосвязей. Более значительное повышение индекса когерентности в дельта и тета диапазонах у пациентов, перенесших геморрагический инсульт, может указывать на более грубые межполушарные нарушения, в сравнении с ишемическим инсультом. In the acute and early recovery periods of cerebral stroke, the correlation between bioelectrical activity of the brain and the clinical picture of the disease is of considerable scientific and practical interest. This article presents the results of a study of clinical, neurological and electroencephalographic parameters, in the acute and early recovery periods of cerebral stroke, in 67 patients aged from 43 to 78. Mild and moderate cognitive impairment as well as anxiety and depressive manifestations were shown among patients in the acute and early recovery periods of cerebral stroke amid the motor and speech disorders. Headache meeting the criteria of tension headache was reported among 61,1% of patients. The bioelectrical activity of the brain was characterised by marked delta and theta activity as well as single sharp waves, commissures mainly in the affected cerebral hemisphere, interhemispheric asymmetry and by increase in the spectrum power towards the predominance of slow waves. The coherence index scores were decreased on all directions, indicating impaired functional interhemispheric connectivity. A greater increase in coherence index in the delta and theta bands among haemorrhagic stroke patients may indicate more severe interhemispheric disturbances compared to ischaemic stroke.

Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

2021 ◽  
pp. 1-11
Author(s):  
Najmeh Pakniyat ◽  
Mohammad Hossein Babini ◽  
Vladimir V. Kulish ◽  
Hamidreza Namazi
Keyword(s):  
Time Series ◽  
Biomedical Science ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Ecg Signals ◽  
Olfactory Stimuli ◽  
Electrocardiogram Ecg ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart’s activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18–22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.

Design of a tribometer for investigating tactile perception

2014 ◽  
Vol 232 (6) ◽  
pp. 773-784 ◽  
Author(s):  
Francesco Massi ◽  
Eric Vittecoq ◽  
Eric Chatelet ◽  
Aurelien Saulot ◽  
Yves Berthier
Keyword(s):  
External Noise ◽  
Tactile Perception ◽  
Sliding Contact ◽  
Clear Understanding ◽  
Tactile Sense ◽  
Friction Forces ◽  
Low Amplitude ◽  
Finger Skin ◽  
The Brain ◽  
Experimental Test Bench

The understanding of the tactile perception mechanism implies the reproduction and measurement of friction forces and vibrations induced by the contact between the skin of human fingers and object surfaces. When a finger moves to scan the surface of an object, it activates the receptors located under the skin allowing the brain to identify surfaces and information about their properties. The information concerning the object surface is affected by the forces and vibrations induced by the friction between the skin and the rubbed object. The vibrations propagate in the finger skin and are converted into electric impulses sent to the brain by the mechanoreceptors. Because of the low amplitude of the induced vibrations, it results quite hard to reproduce the tactile surface scanning and measuring it without affecting measurements by external noise coming from the experimental test-bench. In fact the reproduction of the sliding contact between two surfaces implies the relative motion between them, which is obtained by appropriate mechanisms having a more or less complicated kinematics and including several sliding surfaces (bearings, sliders, etc.). It results quite difficult to distinguish between the vibrations coming from the reproduced sliding and the parasitic noise coming from the other sliding contact pairs. This paper presents the design and validation of a tribometer, named TRIBOTOUCH, allowing for reproducing and measuring friction forces and friction induced vibrations that are basilar for a clear understanding of the mechanisms of the tactile sense.

scholarly journals Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Gerolf Vanacker ◽  
José del R. Millán ◽  
Eileen Lew ◽  
Pierre W. Ferrez ◽  
Ferran Galán Moles ◽  
...  
Keyword(s):  
Control System ◽  
Shared Control ◽  
Eeg Signals ◽  
Intelligent Processing ◽  
Intelligent Wheelchair ◽  
Brain Signals ◽  
Brain Interface ◽  
The Subject ◽  
Electroencephalogram Eeg ◽  
The Brain

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

scholarly journals EEG and Sonic Platforms to Enhance Mindfulness Meditation

2016 ◽  
Vol 5 (9) ◽  
pp. 1
Author(s):  
Caitilin De Berigny ◽  
Freya Zinovieff ◽  
Karen Cochrane ◽  
Youngdong Kim ◽  
Zhepeng Rui
Keyword(s):  
Clinical Practice ◽  
Mindfulness Meditation ◽  
Interactive Design ◽  
Historical Background ◽  
Technology Research ◽  
Interactive Installation ◽  
Interactive Applications ◽  
Physiological Processes ◽  
Electroencephalogram Eeg ◽  
The Brain

<p>This paper explores interactive applications that encourage mindfulness through sensors and novel input technology. Research in psychology and neuroscience demonstrating the benefits of mindfulness is initiating a new movement in interactive design. As cutting edge technologies become more accessible they are being employed to research and explore the practice of mindfulness. We examine three interactive installation artworks that promote mindfulness. In order to contextualize the interactive artworks discussed we first examine the historical background of the Electroencephalogram (EEG). We then discuss the physiological processes of meditation and the history behind the clinical practice of mindfulness. We show how artists and designers employ EEG sensors, to record the electrical activity of the brain to visualize mindfulness meditation practices. Lastly, we conclude the paper by discussing the future of the three artworks.</p>

scholarly journals EEG-based human emotion recognition using entropy as a feature extraction measure

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pragati Patel ◽  
Raghunandan R ◽  
Ramesh Naidu Annavarapu
Keyword(s):  
Emotion Recognition ◽  
Emotional State ◽  
Computer Interface ◽  
Eeg Signal ◽  
Emotion Classification ◽  
Emotion Identification ◽  
Physiological Signal ◽  
Wide Range ◽  
Electrical Activities ◽  
The Brain

AbstractMany studies on brain–computer interface (BCI) have sought to understand the emotional state of the user to provide a reliable link between humans and machines. Advanced neuroimaging methods like electroencephalography (EEG) have enabled us to replicate and understand a wide range of human emotions more precisely. This physiological signal, i.e., EEG-based method is in stark comparison to traditional non-physiological signal-based methods and has been shown to perform better. EEG closely measures the electrical activities of the brain (a nonlinear system) and hence entropy proves to be an efficient feature in extracting meaningful information from raw brain waves. This review aims to give a brief summary of various entropy-based methods used for emotion classification hence providing insights into EEG-based emotion recognition. This study also reviews the current and future trends and discusses how emotion identification using entropy as a measure to extract features, can accomplish enhanced identification when using EEG signal.

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