scholarly journals The analysis of the influence of fractal structure of stimuli on fractal dynamics in fixational eye movements and EEG signal

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Hamidreza Namazi ◽  
Vladimir V. Kulish ◽  
Amin Akrami
Keyword(s):  
Eye Movements ◽  
Visual Stimulus ◽  
Fractal Structure ◽  
Temporal Structure ◽  
Human Vision ◽  
Eeg Signal ◽  
Vision Disorders ◽  
Fixational Eye Movements ◽  
Electroencephalogram Eeg ◽  
The Brain

Abstract One of the major challenges in vision research is to analyze the effect of visual stimuli on human vision. However, no relationship has been yet discovered between the structure of the visual stimulus and the structure of fixational eye movements. This study reveals the plasticity of human fixational eye movements in relation to the ‘complex’ visual stimulus. We demonstrated that the fractal temporal structure of visual dynamics shifts towards the fractal dynamics of the visual stimulus (image). The results showed that images with higher complexity (higher fractality) cause fixational eye movements with lower fractality. Considering the brain, as the main part of nervous system that is engaged in eye movements, we analyzed the governed Electroencephalogram (EEG) signal during fixation. We have found out that there is a coupling between fractality of image, EEG and fixational eye movements. The capability observed in this research can be further investigated and applied for treatment of different vision disorders.

scholarly journals Removing Artifacts From EEG Signal Using Wavelet Transform and Conventional Filters

2021 ◽  
Vol 17 ◽  
pp. 177-183
Author(s):  
Meryem Felja ◽  
Asmae Bencheqroune ◽  
Mohammed Karim ◽  
Ghita Bennis Limas
Keyword(s):  
Wavelet Transform ◽  
Eye Movements ◽  
Wavelet Transforms ◽  
Experimental Results ◽  
Eeg Signal ◽  
Electroencephalogram Eeg ◽  
The Brain

the electroencephalogram (EEG) is a signal of an electrical nature reflecting the neuronal activities of the brain. It is used for the diagnosis of certain cerebral pathologies. However, it becomes more difficult to identify and analyze it when it is corrupted by artifacts of non-cerebral origin such as eye movements, cardiac activities ..., therefore, it is essential to remove these parasitic signals. In literature, there are different techniques for removing artifacts. This paper proposes and discusses a new EEG de-noising technique, based on a combination of wavelet transforms and conventional filters. The experimental results demonstrate that the proposed approach can be an effective tool for removing artifact without suppression of any signal components.

scholarly journals Removing Artifacts From Eeg Signal Using Wavelet Transform and Conventional Filters

2021 ◽  
Vol 15 ◽  
pp. 172-177
Author(s):  
Meryem Felja ◽  
Asmae Bencheqroune ◽  
Mohammed Karim ◽  
Ghita Bennis
Keyword(s):  
Wavelet Transform ◽  
Eye Movements ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Eeg Signal ◽  
Mean Square ◽  
Signal To Noise ◽  
Common Criteria ◽  
Electroencephalogram Eeg ◽  
The Brain

Electroencephalogram (EEG) is a signal of an electrical nature reflecting the neuronal activities of the brain. It is used for the diagnosis of certain cerebral pathologies. However, it becomes more difficult to identify and analyze it when it is corrupted by artifacts of non-cerebral origin such as eye movements, cardiac activities ..., therefore, it is essential to remove these parasitic signals. In literature, there are different techniques for removing artifacts. This paper proposes and discusses a new EEG de-noising technique, based on a combination of wavelet transforms and conventional filters. The results of the proposed method are evaluated using three common criteria: signal-to-noise-ratio (SNR), mean square error (MSE) and cross correletion function (CCF). These experimental results demonstrate that the proposed approach can be an effective tool for removing artifact without suppression of any signal components.

scholarly journals Fixational Eye Movements: Data Augmentation for the Brain?

2019 ◽  
Author(s):  
Beren Millidge
Keyword(s):  
Eye Movements ◽  
Visual Perception ◽  
Data Augmentation ◽  
Predictive Processing ◽  
Neural Data ◽  
Fixational Eye Movements ◽  
Processing Theory ◽  
Invariant Representations ◽  
Neural Underpinnings ◽  
The Brain

Fixational eye movements are ubiquitous and have a large impact on visual perception. Although their physical characteristics and, to some extent, neural underpinnings are well documented, their function, with the exception of preventing visual fading, remains poorly understood. In this paper, we propose that the visual system might utilize the relatively large number of similar slightly jittered images produced by fixational eye movements to help learn robust and spatially invariant representations as a form of neural data augmentation. Additionally, we form a link between effects such as retinal stabilization and predictive processing theory, and argue that they may be best explained under such a paradigm.

DECODING OF SIMPLE AND COMPOUND LIMB MOTOR IMAGERY MOVEMENTS BY FRACTAL ANALYSIS OF ELECTROENCEPHALOGRAM (EEG) SIGNAL

Fractals ◽  
2019 ◽  
Vol 27 (03) ◽  
pp. 1950041 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
TIRDAD SEIFI ALA
Keyword(s):  
Fractal Dimension ◽  
Motor Imagery ◽  
Fractal Analysis ◽  
Brain Activity ◽  
Eeg Signal ◽  
Left Hand ◽  
Right Hand ◽  
Significant Difference ◽  
Electroencephalogram Eeg ◽  
The Brain

One of the major attempts in rehabilitation science is to decode different movements of human using physiological signals. Since human movements are mainly controlled by the brain, decoding of movements by analysis of the brain activity has great importance. In this paper, we apply fractal analysis to Electroencephalogram (EEG) signal in order to decode simple and compound limb motor imagery movements. The fractal dimension of EEG signal is analyzed in case of left hand, right hand, both hands, feet, left hand combined with right foot, and right hand combined with left foot movements. Based on the obtained results, EEG signal experiences the lowest and greatest fractal dimension in case of both hands movement, and feet movement, respectively. Besides obtaining different fractal dimension for EEG signal in case of different movements, no significant difference was observed in fractal dimension of EEG signal between different movements. The method of analysis employed in this research can be widely applied to analysis of EEG signal for decoding of different movements of human.

Eye Anatomy, Eye Movements and Vision

2012 ◽  
pp. 10-20 ◽  
Author(s):  
Fiona Mulvey
Keyword(s):  
Eye Movements ◽  
Human Computer Interaction ◽  
Eye Movement ◽  
Human Perception ◽  
Human Vision ◽  
Emotional States ◽  
Visual World ◽  
Potential Measurement ◽  
Movement Data ◽  
The Brain

This chapter introduces the basics of eye anatomy, eye movements and vision. It will explain the concepts behind human vision sufficiently for the reader to understand later chapters in the book on human perception and attention, and their relationship to (and potential measurement with) eye movements. We will first describe the path of light from the environment through the structures of the eye and on to the brain, as an introduction to the physiology of vision. We will then describe the image registered by the eye, and the types of movements the eye makes in order to perceive the environment as a cogent whole. This chapter explains how eye movements can be thought of as the interface between the visual world and the brain, and why eye movement data can be analysed not only in terms of the environment, or what is looked at, but also in terms of the brain, or subjective cognitive and emotional states. These two aspects broadly define the scope and applicability of eye movements technology in research and in human computer interaction in later sections of the book.

Dynamic Analysis of Human Brain in the Pain State by Electroencephalography

2021 ◽  
Author(s):  
Mahsa Tavasoli ◽  
zahra einalou ◽  
Reza Akhondzadeh
Keyword(s):  
Brain Regions ◽  
Eeg Signal ◽  
Neural Network Classifier ◽  
Unpleasant Sensation ◽  
Quantification Analysis ◽  
Pain State ◽  
Dynamical Features ◽  
Pain Detection ◽  
Electroencephalogram Eeg ◽  
The Brain

Abstract Objective Pain is an unpleasant sensation that is important in all therapeutic conditions. So far, some studies have focused on pain assessment and cognition through different tests and methods. Considering the occurrence of pain causes, along with the activation of a long network in brain regions, recognizing the dynamical changes of the brain in pain states is helpful for pain detection using the electroencephalogram (EEG) signal. Therefore, the present study addressed the above-mentioned issue by applying EEG at the time of inducing phasic pain. Results Phasic pain was produced using coldness and then dynamical features via EEG were analyzed by the Recurrence Quantification Analysis (RQA) method, and finally, the Rough neural network classifier was utilized for achieving accuracy regarding detecting and categorizing pain and non-pain states, which was 95.25\(\pm\)4%. The simulation results confirmed that cerebral behaviors are detectable during pain. In addition, the high accuracy of the classifier for evaluating the dynamical features of the brain during pain occurrence is one of the most merits of the proposed method. Eventually, pain detection can improve medical methods.

Temporal Dynamics of Figure-Ground Segregation in Human Vision

2007 ◽  
Vol 97 (1) ◽  
pp. 951-957 ◽  
Author(s):  
Peter Neri ◽  
Dennis M. Levi
Keyword(s):  
Visual Stimulus ◽  
Temporal Dynamics ◽  
Human Perception ◽  
Human Vision ◽  
Reverse Correlation ◽  
Single Neurons ◽  
Neural Signals ◽  
Temporal Window ◽  
Electrophysiological Studies ◽  
The Brain

The segregation of figure from ground is arguably one of the most fundamental operations in human vision. Neural signals reflecting this operation appear in cortex as early as 50 ms and as late as 300 ms after presentation of a visual stimulus, but it is not known when these signals are used by the brain to construct the percepts of figure and ground. We used psychophysical reverse correlation to identify the temporal window for figure-ground signals in human perception and found it to lie within the range of 100–160 ms. Figure enhancement within this narrow temporal window was transient rather than sustained as may be expected from measurements in single neurons. These psychophysical results prompt and guide further electrophysiological studies.

scholarly journals Closed loop motor-sensory dynamics in human vision

2019 ◽  
Author(s):  
Liron Gruber ◽  
Ehud Ahissar
Keyword(s):  
Eye Movements ◽  
Visual Processing ◽  
Closed Loop ◽  
Open Loop ◽  
Visual Input ◽  
Human Vision ◽  
Visual Inputs ◽  
Eye Motion ◽  
Slow Eye Movements ◽  
The Brain

AbstractVision is obtained with a continuous motion of the eyes. The kinematic analysis of eye motion, during any visual or ocular task, typically reveals two (kinematic) components: saccades, which quickly replace the visual content in the retinal fovea, and drifts, which slowly scan the image after each saccade. While the saccadic exchange of regions of interest (ROIs) is commonly considered to be included in motor-sensory closed-loops, it is commonly assumed that drifts function in an open-loop manner, that is, independent of the concurrent visual input. Accordingly, visual perception is assumed to be based on a sequence of open-loop processes, each initiated by a saccade-triggered retinal snapshot. Here we directly challenged this assumption by testing the dependency of drift kinematics on concurrent visual inputs using real-time gaze-contingent-display. Our results demonstrate a dependency of the trajectory on the concurrent visual input, convergence of speed to condition-specific values and maintenance of selected drift-related motor-sensory controlled variables, all strongly indicative of drifts being included in a closed-loop brain-world process, and thus suggesting that vision is inherently a closed-loop process.Author summaryOur eyes do not function like cameras; it has long been known that we are actively scanning our visual environment in order to see. Moreover, it is commonly accepted that our fast eye movements, saccades, are controlled by the brain and are affected by the sensory input. However, our slow eye movements, the ocular drifts, are often ignored when visual acquisition is analyzed. Accordingly, visual processing is typically assumed to be based on computations performed on saccade-triggered snapshots of the retinal state. Our work strongly challenges this model and provides significant evidence for an alternative model, a cybernetic one. We show that the dynamics of the ocular drifts do not allow, and cannot be explained by, open loop visual acquisition. Instead, our results suggest that visual acquisition is part of a closed-loop process, which dynamically and continuously links the brain to its environment.

scholarly journals Analisis Perbedaan Pola Sinyal EEG Berdasarkan Jenis Kelamin Yang Berbeda Saat Numerical Stroop Task

2018 ◽  
Vol 8 (1) ◽  
pp. 107
Author(s):  
Riswandha Latu Dimas ◽  
Catur Atmaji
Keyword(s):  
Stroop Task ◽  
Cognitive Process ◽  
Energy Analysis ◽  
Peak Time ◽  
Eeg Signal ◽  
Observation Process ◽  
Time And Energy ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Numerical Stroop Task

Cognitive process show how brain work from stimulus reception until stimuls reaction. With electroencephalogram (EEG) device, cognate process can be observerd in brain signal or EEG signal form. In cognitive process different kind of stimulus could affect generated brain signal. Also, given interference in cognitive prcess could affect brain signal. In this research, conducted observation whether gender difference has effect in cognitive process. Numerical stroop task with three kinds of conditions (congruence, incongruence, and neutral) are used as reference in signal observation process which is generated when the cognitive process in difference genders are done. The resulting EEG signal then conducted three kinds of analysis that is ERP analysis, reaction time, and energy analysis. The result of ERP analysis show both subject class have difference in response time that indicated with P3 peak time. On average, respons time in female (kongruent = 623,34 ms; inkongruent = 645,18 ms ; neutral = 614,91 ms)subject class is faster than male (kongruent = 709,67 ms; inkongruent = 745,00 ms; neutral =715,37 ms) subject class. Energy analysis show when numerical stroop task takes place, left side of the brain (51,36%) and cetral side of the brain (50,65%) more dominant than others parts of the brain.

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