Parametric bispectral estimation of EEG signals in different functional states of the brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

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NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

International Conference on Technics Technologies and Education ◽

10.15547/ictte.2019.02.095 ◽

2019 ◽

pp. 139-145

Author(s):

Selma Büyükgöze

Keyword(s):

Brain Computer Interface ◽

Computer Interface ◽

Electrode Placement ◽

Eeg Signals ◽

Non Invasive ◽

Brain Signals ◽

Brain States ◽

The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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Identification of Brain Electrical Activity Related to Head Yaw Rotations

Sensors ◽

10.3390/s21103345 ◽

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.

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Information-based decoding of the coupling among human brain activity and movement paths

Technology and Health Care ◽

10.3233/thc-202744 ◽

2021 ◽

pp. 1-10

Author(s):

Shahul Mujib Kamal ◽

Norazryana Mat Dawi ◽

Hamidreza Namazi

Keyword(s):

Brain Activity ◽

Point Of View ◽

Physiological Signals ◽

Human Walking ◽

Eeg Signals ◽

Rehabilitation Science ◽

Information Contents ◽

First Time ◽

The Brain ◽

Brain Reaction

BACKGROUND: Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements. OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view. METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents. RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.

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Parametric Bispectral Estimation of EEG Signals in Disorders of Consciousness

2019 Chinese Control Conference (CCC) ◽

10.23919/chicc.2019.8865425 ◽

2019 ◽

Author(s):

Xile Wei ◽

Wenli Wang ◽

Lihui Cai ◽

Meili Lu ◽

Guosheng Yi ◽

...

Keyword(s):

Disorders Of Consciousness ◽

Eeg Signals ◽

Bispectral Estimation

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

Technology and Health Care ◽

10.3233/thc-213136 ◽

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.

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Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

Computational Intelligence and Neuroscience ◽

10.1155/2007/25130 ◽

2007 ◽

Vol 2007 ◽

pp. 1-12 ◽

Cited By ~ 72

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.

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Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink Sheikh Md. Rabiul Islam1, Md. Shakibul Islam2

Indian Journal of Signal Processing ◽

10.54105/ijsp.c1008.081321 ◽

2021 ◽

pp. 1-7

Author(s):

Sheikh Md. Rabiul Islam ◽

◽

Md. Shakibul Islam ◽

Keyword(s):

Signal Generation ◽

Neural Mass Model ◽

Pathophysiological Mechanism ◽

Eeg Signal ◽

Eeg Signals ◽

Mass Model ◽

Detailed Model ◽

Simulation Based ◽

Neural Mass ◽

The Brain

The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.

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Pre-stimulus phase and amplitude regulation of phase-locked responses are maximized in the critical state

eLife ◽

10.7554/elife.53016 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 1

Author(s):

Arthur-Ervin Avramiea ◽

Richard Hardstone ◽

Jan-Matthis Lueckmann ◽

Jan Bím ◽

Huibert D Mansvelder ◽

...

Keyword(s):

Computational Model ◽

Critical State ◽

Global Dynamics ◽

Critical Dynamics ◽

Neuronal Responses ◽

Scale Free ◽

Stimulus Response ◽

Functional States ◽

The Brain

Understanding why identical stimuli give differing neuronal responses and percepts is a central challenge in research on attention and consciousness. Ongoing oscillations reflect functional states that bias processing of incoming signals through amplitude and phase. It is not known, however, whether the effect of phase or amplitude on stimulus processing depends on the long-term global dynamics of the networks generating the oscillations. Here, we show, using a computational model, that the ability of networks to regulate stimulus response based on pre-stimulus activity requires near-critical dynamics—a dynamical state that emerges from networks with balanced excitation and inhibition, and that is characterized by scale-free fluctuations. We also find that networks exhibiting critical oscillations produce differing responses to the largest range of stimulus intensities. Thus, the brain may bring its dynamics close to the critical state whenever such network versatility is required.

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