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.

scholarly journals Motor imagery recognition in electroencephalograms using convolutional neural networks

2020 ◽  
Vol 44 (3) ◽  
pp. 482-487
Author(s):  
A.D. Bragin ◽  
V.G. Spitsyn
Keyword(s):  
Neural Networks ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Brain Activity ◽  
Processing Unit ◽  
Eeg Signal ◽  
Good Tool ◽  
Markov Transition ◽  
Transition Field ◽  
The Brain

Electroencephalography is a widespread method to record brain signals with the use of electrodes located on the surface of the head. This method of recording the brain activity has become popular because it is relatively cheap, compact, and does not require implanting the electrodes directly into the brain. The article is devoted to a problem of recognition of motor imagery by electroencephalogram signals. The nature of such signals is complex. Characteristics of electroencephalograms are individual for every person, also depending on their age and mental state, as well as the presence of noise and interference. The multitude of these parameters should be taken into account when analyzing encephalograms. Artificial neural networks are a good tool for solving this class of problems. Their application allows combining the tasks of extracting, selecting and classifying features in one signal processing unit. Electroencephalograms are time signals and we note that Gramian Angular Fields and Markov Transition Field transforms are used to represent time series in the form of images. The article shows the possibility of using the Gramian Angular Fields and Markov Transition Field transformations of the electroencephalogram (EEG) signal for motor imagery recognition using examples of imaginary movements with the right and left hand, also studying the effect of the resolution of Gramian Angular Fields and Markov Transition Field images on the classification accuracy. The best classification accuracy of the EEG signal into the motion and state-of-rest classes is about 99%. In future, the research results can be applied in constructing the brain-computer interface.

scholarly journals Classification of Motor Imagery Based EEG Signals

2019 ◽  
Vol 8 (9) ◽  
pp. 1012-1020
Keyword(s):  
Motor Imagery ◽  
Learning Algorithm ◽  
Human Subjects ◽  
Brain Activity ◽  
Back Propagation ◽  
Support Vector ◽  
Raw Data ◽  
Left Hand ◽  
Right Hand ◽  
Eeg Data

Brain Computer Interface (BCI) enable the user to interact with system only through brain activity, usually measured by Electroencephalography (EEG). BCI systems additionally offers analysis of Motor Imagery EEG, which may be appeared, is a novel way of communication for the patients who are physically disabled. Motor Imagery based EEG data (left hand, right hand, or foot) movements supplied by BCI Competition IV dataset1. The data signals were band-pass filtered between 0.05 and 200Hz and sampled at 100Hz. The features extracted from the raw data with respect to time and frequency domain of required channels. Motor Imagery based EEG (left hand, right hand or foot) data classified using machine learning algorithm namely Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) for four normal human subjects (a, b, f, g). Analysis of motor imagery-based EEG data was studied using EEGLAB toolbox. Selected data are presented from raw data in channel data (scroll), representation of channel location in 2D and 3D form, channel spectra and maps and channel properties.

Analysis of Motor Imagery EEG Based on Hilbert-Huang Transform

2014 ◽  
Vol 998-999 ◽  
pp. 833-837
Author(s):  
Xiao Lin Zhu ◽  
Jian Ping Liu ◽  
Xiao Nan Zhang
Keyword(s):  
Motor Imagery ◽  
Recognition Rate ◽  
Eeg Signal ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Left Hand ◽  
Time Frequency ◽  
The Hilbert Transform ◽  
Electroencephalogram Eeg ◽  
Emd Method

Based on Hilbert-Huang Transform (HHT) theory, we present a method to analyze the electroencephalogram (EEG) signal of right and left hand motor imagery. Firstly, EMD method decomposed EEG signal into a group of intrinsic mode functions (IMFs). The first three IMFs were extracted to denoise. We adopt endpoint Mirror Extension method to relieve the influence on subsequent processing brought by endpoint effect. According to the Hilbert transform, we can obtain the time-frequency distribution. The energy of the first three components is selected as the input of SVM. The results show that EMD is an efficient method to analyze the EEG signal. The proposed method obtains an ideal recognition rate.

ESTIMATING OF BRAIN DEVELOPMENT IN NEWBORNS BY FRACTAL ANALYSIS OF SLEEP ELECTROENCEPHALOGRAPHIC (EEG) SIGNAL

Fractals ◽  
2019 ◽  
Vol 27 (03) ◽  
pp. 1950021 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI
Keyword(s):  
Fractal Dimension ◽  
Brain Development ◽  
Complexity Analysis ◽  
Imaging Techniques ◽  
Brain Activity ◽  
Fractal Theory ◽  
Eeg Signal ◽  
Nonlinear Structure ◽  
Research Areas ◽  
The Brain

Analysis of the brain development is one of the major research areas in human neuroscience. In order to analyze the human brain development, scientists employ different brain imaging techniques. One of the typical techniques to measure the brain activity is electroencephalography (EEG). In this paper, we do complexity analysis on the EEG signal recorded from the newborns during their sleep, in different weeks of post conception. We analyze how the nonlinear structure of EEG signal changes for newborns with their ages by using fractal theory. The result of our analysis showed that the EEG signals for newborn in 45 weeks have the highest fractal dimension. The lowest fractal dimension of EEG signal was obtained for newborns in 36 weeks. Based on our analysis, we conclude that the complexity of brain signal significantly changes with the newborn age. The proposed method is not limited to the analysis of the brain development, and can be applied to investigate the brain activity in different tasks.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
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.

AGE-BASED VARIATIONS OF FRACTAL STRUCTURE OF EEG SIGNAL IN PATIENTS WITH EPILEPSY

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850051 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI
Keyword(s):  
Fractal Dimension ◽  
Brain Research ◽  
Age Groups ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals ◽  
Important Challenge ◽  
Patients With Epilepsy ◽  
Process Complexity ◽  
Electroencephalogram Eeg

It is known that aging affects neuroplasticity. On the other hand, neuroplasticity can be studied by analyzing the electroencephalogram (EEG) signal. An important challenge in brain research is to study the variations of neuroplasticity during aging for patients suffering from epilepsy. This study investigates the variations of the complexity of EEG signal during aging for patients with epilepsy. For this purpose, we employed fractal dimension as an indicator of process complexity. We classified the subjects in different age groups and computed the fractal dimension of their EEG signals. Our investigations showed that as patients get older, their EEG signal will be more complex. The method of investigation that has been used in this study can be further employed to study the variations of EEG signal in case of other brain disorders during aging.

scholarly journals Mentally Challenged Children’s Performance Comparison to Evaluate their Motor Function of the Brain – By Applying Finger Tapping Sub-test Test of Neuropsychological Battery

2017 ◽  
Vol 4 (4) ◽  
Author(s):  
Mr. Anjan N. Patel ◽  
Dr. D.J Panchal
Keyword(s):  
Mental Retardation ◽  
Motor Function ◽  
Performance Comparison ◽  
Finger Tapping ◽  
Neuropsychological Battery ◽  
Left Hand ◽  
Similar Performance ◽  
Right Hand ◽  
Significant Difference ◽  
Finger Tapping Test

“Mentally challenged children’s performance comparison to evaluate their brain’s motor function – by applying finger tapping subtest test of neuropsychological battery” this is a research problem to know that mental retardation is affecting their motor co-ordination function on not”. Neuro-psychological assessment test battery which was developed by NIMHANS, Bangalore in the year of 2004 and its sub-test Finger tapping test was administered for research. By random sampling method, samples were collected from B.M Institute of Mental Health, Ahmedabad in the year of 2012. Based on Government Civil hospital’s IQ certificate 75 mild category of MR children of above and below graduate parents were taken for research with prior permission. Mental Retardation based on various diagnosis like; Down syndrome, microcephaly, hydrocephaly, trisomy-13, trisomy-18 or multiple disabilities are included. Flowingly, Children’s age group and gender are also kept different to compare their performance on finger tapping test. The results shows the “t” value of Neuropsychological functions of finger tapping test (Right hand) of mild children of different educational level of parents is 0.12. The mean scores of finger tapping test (Right hand) of mild children of above and below graduate parents were found 36.08 and 36.38 respectively with SD 8.50 and 12.56. The results indicate that significant difference does not exist between mild children of below and above graduate parents with regard to finger tapping test (Right hand). In the light of the hypothesis that there will be no significant difference between mild children of below and above graduate parents on finger tapping test (Right hand). It implies that mild children of below and above graduate parents have no significant difference of performance on finger tapping test (Right hand). Based on the result it depicts that mild children of below and above graduate parents’ children have similar performance on finger tapping test (Right hand). The results shows the “t” value of Neuropsychological functions of finger tapping test (Left hand) of mild children of educational level of parents is 0.55. The mean scores of finger tapping test (Left hand) of mild children of above graduate parents were found 34.49 and 35.92 respectively with SD 9.18 and 13.03. The results indicate that significant difference exist between mild children of below graduate parents and mild children of above graduate parents with regard to finger tapping test (Left hand). In the light of the hypothesis that there will be no significant difference between mild children of below and above graduate parents on finger tapping test (Left hand). It implies that mild children of below and above graduate parents have no significant difference on finger tapping test (left hand). Mild children of above and below graduate parent’s children have similar performance on finger tapping test (left hand). On the basis of result it is concluded that Mild MR category of above and below graduate parent’s children have similar performance on Finger tapping test in both Right and Left hand. It depicts that these children are fair in their motor function of motor speed and co-ordination. 75 children’s mental retardation does not affected on their brain’s motor function performance.

scholarly journals Increased signal diversity/complexity of spontaneous EEG, but not evoked EEG responses, in ketamine-induced psychedelic state in humans

2019 ◽  
Author(s):  
Nadine Farnes ◽  
Bjørn E. Juel ◽  
André S. Nilsen ◽  
Luis G. Romundstad ◽  
Johan F. Storm
Keyword(s):  
Brain Activity ◽  
Altered States Of Consciousness ◽  
Altered States ◽  
Eeg Signal ◽  
Eyes Closed ◽  
Electrical Brain Activity ◽  
States Of Consciousness ◽  
Significant Difference ◽  
Eyes Open ◽  
Signal Diversity

AbstractObjectiveHow and to what extent electrical brain activity is affected in pharmacologically altered states of consciousness, where it is mainly the phenomenological content rather than the level of consciousness that is altered, is not well understood. An example is the moderately psychedelic state caused by low doses of ketamine. Therefore, we investigated whether and how measures of evoked and spontaneous electroencephalographic (EEG) signal diversity are altered by sub-anaesthetic levels of ketamine compared to normal wakefulness, and how these measures relate to subjective assessments of consciousness.MethodsHigh-density electroencephalography (EEG, 62 channels) was used to record spontaneous brain activity and responses evoked by transcranial magnetic stimulation (TMS) in 10 healthy volunteers before and after administration of sub-anaesthetic doses of ketamine in an open-label within-subject design. Evoked signal diversity was assessed using the perturbational complexity index (PCI), calculated from the global EEG responses to local TMS perturbations. Signal diversity of spontaneous EEG, with eyes open and eyes closed, was assessed by Lempel Ziv complexity (LZc), amplitude coalition entropy (ACE), and synchrony coalition entropy (SCE).ResultsAlthough no significant difference was found in the index of TMS-evoked complexity (PCI) between the sub-anaesthetic ketamine condition and normal wakefulness, all the three measures of spontaneous EEG signal diversity showed significantly increased values in the sub-anaesthetic ketamine condition. This increase in signal diversity also correlated with subjective assessment of altered states of consciousness. Moreover, spontaneous signal diversity was significantly higher when participants had eyes open compared to eyes closed, both during normal wakefulness and during influence of sub-anaesthetic ketamine doses.ConclusionThe results suggest that PCI and spontaneous signal diversity may be complementary and potentially measure different aspects of consciousness. Thus, our results seem compatible with PCI being indicative of the brain’s ability to sustain consciousness, as indicated by previous research, while it is possible that spontaneous EEG signal diversity may be indicative of the complexity of conscious content. The observed sensitivity of the latter measures to visual input seems to support such an interpretation. Thus, sub-anaesthetic ketamine may increase the complexity of both the conscious content (experience) and the brain activity underlying it, while the level, degree, or general capacity of consciousness remains largely unaffected.

The Effect of Crystal Dependence on Brain Activity Related to the Perception of Pleasure Using fMRI

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Ali Yoonessi ◽  
Seyed Amir Hossein Batouli ◽  
Iman Ahmadnezhad ◽  
Hamid Soltanian-zadeh
Keyword(s):  
Case Control Study ◽  
Brain Activity ◽  
Economic Status ◽  
Smoking History ◽  
Control Group ◽  
Human Society ◽  
Significant Difference ◽  
History Of ◽  
The Brain ◽  
Control Study

Background: Addiction is currently one of the problems of human society. Drug abuse is one of the most important issues in the field of addiction. Methamphetamine (crystal) is one of the drugs that has been abused in recent decades. Methods: In this case-control study, 10 individuals aged 20 to 40 years old with at least 2 years of experience of methamphetamine consumption without any history of drug use or other stimulants from clients and drug withdrawal centers in Tehran City, and 10 healthy volunteers were selected. Age, social status, and economic status of addicts were included in the fMRI apparatus, and 90 selected pleasurable, non-pleasurable, and neutral images (IAPS) were displayed by the projector through an event-related method. The playback time of each photo was 3 s, and after this process, the person outside the device, without the time limit selected the enjoyable and unpleasant images. Results: The results showed that there was no significant difference between the groups in terms of age, alcohol use, and smoking history (P < 0.05). There was no significant difference in terms of the age at first use between members of the methamphetamine-dependent group. Also, the methamphetamine-dependent group showed more brain activity in their pre-center and post-center gyrus than the normal (control) group. Conclusions: According to the results obtained in this study, in general, it can be concluded that there are some areas in the brain of addicts that are activated when watching pleasant photos, while these areas are not active in the brains of normal people.

Decoding the coupling between the brain and skin reactions in auditory stimulation by information-based analysis of EEG and GSR signals

2021 ◽  
pp. 1-10
Author(s):  
Najmeh Pakniyat ◽  
Hamidreza Namazi
Keyword(s):  
Brain Activity ◽  
Skin Resistance ◽  
Research Area ◽  
Important Research ◽  
Skin Reactions ◽  
Analysis Technique ◽  
Information Contents ◽  
Important Research Area ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: The analysis of brain activity in different conditions is an important research area in neuroscience. OBJECTIVE: This paper analyzed the correlation between the brain and skin activities in rest and stimulations by information-based analysis of electroencephalogram (EEG) and galvanic skin resistance (GSR) signals. METHODS: We recorded EEG and GSR signals of eleven subjects during rest and auditory stimulations using three pieces of music that were differentiated based on their complexity. Then, we calculated the Shannon entropy of these signals to quantify their information contents. RESULTS: The results showed that music with greater complexity has a more significant effect on altering the information contents of EEG and GSR signals. We also found a strong correlation (r= 0.9682) among the variations of the information contents of EEG and GSR signals. Therefore, the activities of the skin and brain are correlated in different conditions. CONCLUSION: This analysis technique can be utilized to evaluate the correlation among the activities of various organs versus brain activity in different conditions.

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