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.

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

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 Compressibility of High-Density EEG Signals in Stroke Patients

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4107 ◽  
Author(s):  
Nadia Mammone ◽  
Simona De Salvo ◽  
Cosimo Ieracitano ◽  
Silvia Marino ◽  
Emanuele Cartella ◽  
...  
Keyword(s):  
Electrical Activity ◽  
Bayesian Learning ◽  
Similarity Index ◽  
Structural Similarity ◽  
High Density ◽  
Critical Event ◽  
Compression Rate ◽  
Eeg Signals ◽  
Neural Connections ◽  
The Brain

Stroke is a critical event that causes the disruption of neural connections. There is increasing evidence that the brain tries to reorganize itself and to replace the damaged circuits, by establishing compensatory pathways. Intra- and extra-cellular currents are involved in the communication between neurons and the macroscopic effects of such currents can be detected at the scalp through electroencephalographic (EEG) sensors. EEG can be used to study the lesions in the brain indirectly, by studying their effects on the brain electrical activity. The primary goal of the present work was to investigate possible asymmetries in the activity of the two hemispheres, in the case one of them is affected by a lesion due to stroke. In particular, the compressibility of High-Density-EEG (HD-EEG) recorded at the two hemispheres was investigated since the presence of the lesion is expected to impact on the regularity of EEG signals. The secondary objective was to evaluate if standard low density EEG is able to provide such information. Eighteen patients with unilateral stroke were recruited and underwent HD-EEG recording. Each EEG signal was compressively sensed, using Block Sparse Bayesian Learning, at increasing compression rate. The two hemispheres showed significant differences in the compressibility of EEG. Signals acquired at the electrode locations of the affected hemisphere showed a better reconstruction quality, quantified by the Structural SIMilarity index (SSIM), than the EEG signals recorded at the healthy hemisphere (p < 0.05), for each compression rate value. The presence of the lesion seems to induce an increased regularity in the electrical activity of the brain, thus an increased compressibility.

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.

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 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 Methods for the Analysis of EEG signals: A Review

2021 ◽  
Vol 9 (9) ◽  
pp. 873-876
Author(s):  
Malika Garg
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Electrical Activity ◽  
Signal Analysis ◽  
Eeg Signal ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Processing Techniques ◽  
The Brain ◽  
Eeg Signal Analysis

Abstract: Electroencephalography (EEG) helps to predict the state of the brain. It tells about the electrical activity going on in the brain. Difference of the surface potential evolved from various activities get recorded as EEG. The analysis of these EEG signals is of utmost importance to solve the problems related to the brain. Signal pre-processing, feature extraction and classification are the main steps of the EEG signal analysis. In this article we discussed various processing techniques of EEG signals. Keywords: EEG, analysis, signal processing, feature extraction, classification

scholarly journals Chloride dynamics alter the input-output properties of neurons

2019 ◽  
Author(s):  
Christopher B. Currin ◽  
Andrew J. Trevelyan ◽  
Colin J. Akerman ◽  
Joseph V. Raimondo
Keyword(s):  
Chloride Concentration ◽  
Reversal Potential ◽  
Chloride Ions ◽  
Inhibitory Synapses ◽  
Synaptic Inhibition ◽  
Output Function ◽  
Input Output ◽  
Subcellular Targeting ◽  
The Brain ◽  
Neuronal Output

AbstractFast synaptic inhibition is a critical determinant of neuronal output, with subcellular targeting of synaptic inhibition able to exert different transformations of the neuronal input-output function. At the receptor level, synaptic inhibition is primarily mediated by chloride-permeable Type A GABA receptors. Consequently, dynamics in the neuronal chloride concentration can alter the functional properties of inhibitory synapses. How differences in the spatial targeting of inhibitory synapses interact with intracellular chloride dynamics to modulate the input-output function of neurons is not well understood. To address this, we developed computational models of multi-compartment neurons that incorporate experimentally parametrised mechanisms to account for neuronal chloride influx, diffusion, and extrusion. We found that synaptic input (either excitatory, inhibitory, or both) can lead to subcellular variations in chloride concentration, despite a uniform distribution of chloride extrusion mechanisms. Accounting for chloride changes resulted in substantial alterations in the neuronal input-output function. This was particularly the case for peripherally targeted dendritic inhibition where dynamic chloride compromised the ability of inhibition to offset neuronal input-output curves. Our simulations revealed that progressive changes in chloride concentration mean that the neuronal input-output function is not static but varies significantly as a function of the duration of synaptic drive. Finally, we found that the observed effects of dynamic chloride on neuronal output were entirely mediated by changes in the dendritic reversal potential for GABA. Our findings provide a framework for understanding the computational effects of chloride dynamics on dendritically targeted synaptic inhibition.Author SummaryThe fundamental unit of computation in the brain is the neuron, whose output reflects information within the brain. A determining factor in the transfer and processing of information in the brain is the modulation of activity by inhibitory synaptic inputs. Fast synaptic inhibition is mediated by the neurotransmitter GABA binding to GABAA receptors, which are permeable to chloride ions. How changes in chloride ion concentration affect neuronal output is an important consideration for information flow in the brain that is currently not being thoroughly investigated. In this research, we used multi-compartmental models of neurons to link the deleterious effects that accumulation of chloride ions can have on inhibitory signalling with changes in neuronal ouput. Together, our results highlight the importance of accounting for changes in chloride concentration in theoretical and computer-based models that seek to explore the computational properties of inhibition.

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