Neural Engineering / Neuroengineering / Brain Engineering
Mohammad Reza Nazari; Mohammad Reza Daliri; Ali Motie Nasrabadi
Volume 16, Issue 1 , May 2022, , Pages 51-62
Abstract
Visual attention as a cognitive factor plays a significant role in the processing of higher-order mental information that happens in the brain and affects brain activity in various areas of the visual cortex. Among the various recording systems, local field potentials, due to their stability, robustness, ...
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Visual attention as a cognitive factor plays a significant role in the processing of higher-order mental information that happens in the brain and affects brain activity in various areas of the visual cortex. Among the various recording systems, local field potentials, due to their stability, robustness, and frequency content have received interest in brain structure and cognitive processing research, as well as brain-computer interface (BCI) systems. Hence, the extraction and interpretation of information from local field potential (LFP) signals during visual attention has been considered to control cognitive systems. Cross-frequency coupling (CFC) as one of the information encoding strategies in the brain plays a functional role in perception, working memory, and visual attention tasks. However, the role of CFC as informative features for spatial attention decoding has not been adequately investigated. This paper aims to examine spatial attention decoding using LFP signals recorded from the monkey middle temporal area (MT). For this purpose, phase-phase and phase-amplitude coupling features and machine learning algorithms have been employed. The results show that the highest decoding performance was achieved by applying selected optimal features and the support vector machine classifier (90.36%). Moreover, among the selected features, gamma-delta, gamma-alpha, and beta-delta coupling contain the most cognitive information and the most effective features to improve the decoding performance of spatial attention in the visual system. Generally, the results suggest that cross-frequency coupling of LFP signals contains significant information in spatial attention tasks, and can be used as a suitable alternative to the time-frequency features of brain signals in cognitive BCI systems.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Tahereh Taleei; Ali Motie Nasrabadi
Volume 15, Issue 4 , March 2022, , Pages 341-353
Abstract
To interact with such an ever-changing environment in which we live, our brain requires to continuously generate and update expectations about relevant upcoming events and their estimation for the corresponding sensory and motor responses. The goal of this study is to investigate the connectivity in ...
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To interact with such an ever-changing environment in which we live, our brain requires to continuously generate and update expectations about relevant upcoming events and their estimation for the corresponding sensory and motor responses. The goal of this study is to investigate the connectivity in time perception in the two predictable and unpredictable conditions. The data needed for the study from EEG signals recorded from the existing database that included an experiment was conducted on 29 healthy subjects in the two predictable and unpredictable conditions and in 4 delays of 83, 150, 400, 800 ms for each person was done. To estimate the functional connectivity between brain regions, we used the phase lag index method. This method is used to detect time perception in two conditions, predictable and unpredictable events. Initially, by comparing the two conditions in 4 delays was shown that more of the differences were in the gamma, beta, and theta bands. Also, the significant difference between the delays in the predictable condition was greater than the unpredictable condition. Then, the difference between the two conditions in each delay was discussed. The results showed a significant difference in all delays. The alpha band in the unpredictable condition in 400-ms delay, the number of connectivity between occipital and temporal regions was increased and stronger, and also the mean of the unpredictable connectivity was higher than predictable condition. In the delta band for 150, 400 and 800-ms delays, there was connectivity between the central and frontal regions, whereas in 83-ms-delay there was stronger connectivity between the central and prefrontal regions. The right hemisphere of the prefrontal is important in time perception. At the longest delay (800 ms), in three bands, delta, theta, and beta, connectivity decreased in both conditions compared to the other delays.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Hessam Ahmadi; Emad Fatemizadeh; Alimotie Nasrabadi
Volume 14, Issue 3 , October 2020, , Pages 235-249
Abstract
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging technique for analyzing the brain functions through low-frequency fluctuations called the Blood-Oxygen-Level-Dependent (BOLD) signals. Measurement of the functional connectivity in brain networks is usually done by the fMRI time-series ...
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Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging technique for analyzing the brain functions through low-frequency fluctuations called the Blood-Oxygen-Level-Dependent (BOLD) signals. Measurement of the functional connectivity in brain networks is usually done by the fMRI time-series through Pearson Correlation Coefficients (PCC). As the PCC shows linear dependencies, in this study, non-linear relationships in the fMRI signals of the patients with Alzheimer's Disease (AD) were investigated using the kernel trick method. Kernel trick approach maps the input information into a higher dimension space and implements the linear calculations in a new space that is proportionate to the non-linear relationships in the primary space. After generating the weighted undirected brain graphs based on the Automated Anatomical Labeling (AAL) atlas, different kernel functions with different parameters were applied. Then the graph global measures including degree, strength, small-worldness, modularity, and efficiencies features were computed and the non-parametric permutation test was performed. According to the results, the kernel trick method showed more significant differences with AD and healthy subjects in comparison with the simple PCC and it could be because of the non-linear correlations that are not captured by the PCC. Among different kernel functions, the Polynomial function had the best performance. Applying this kernel, the classification was done by the Support Vector Machine (SVM) classifier. The achieved accuracy was equal to 98.68±0.79%. The Occipital and Temporal lobes and also the Default Mode Network (DMN) were analyzed and the kernel trick method showed more significant differences in all of them. It is worthwhile to mention that the right and left Angular areas of DMN showed no significant changes in none of the methods and it could be concluded that the AD does not affect this areas effectively.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mehdi Abdossalehi; Ali Motie Nasrabadi; Seyed Mohammad Firouzabadi
Volume 7, Issue 2 , June 2013, , Pages 143-153
Abstract
In this study, electroencephalogram (EEG) signals have been analyzed in positive, negative and neutral emotions. Here it is supposed that the brain has different independent sources during an emotional activity which will be extractable by Independent Component Analysis (ICA) algorithm. For resolving ...
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In this study, electroencephalogram (EEG) signals have been analyzed in positive, negative and neutral emotions. Here it is supposed that the brain has different independent sources during an emotional activity which will be extractable by Independent Component Analysis (ICA) algorithm. For resolving the illposeness problem of extracted components by ICA algorithm, first these sources were sorted by Shannon entropy and then the features of Katz fractal dimension and the first local minimum of the mutual information based on the time delay (tau) have been extracted for representing determinism. The results show that the determinism ratio of the sorted sources has significant difference during the time in three emotional states: positive, negative and neutral. The determinism ratio increases in neutral, negative and positive emotional states, respectively.
Bioelectromagnetics
Hadi Tavakoli; Ali Motie Nasrabadi; Seyed Mohammad Firouzabadi; Mehri Kaviyani Moghaddam
Volume 6, Issue 2 , June 2012, , Pages 123-131
Abstract
During recent years, the environment has been enormously changed by the wide range of magnetic fields. Therefore, comprehensive studies are being done for investigating their biological effects. The effects such as inhibition of bioelectric activity of neurons which is shown by evidence, like decreasing ...
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During recent years, the environment has been enormously changed by the wide range of magnetic fields. Therefore, comprehensive studies are being done for investigating their biological effects. The effects such as inhibition of bioelectric activity of neurons which is shown by evidence, like decreasing in the firing frequency or decreasing in the amplitude of action potential, have been shown. To notify and investigate these effects, the theory of “biological windows” have been proposed and considered. The effects of amplitude and/or frequency of magnetic field have been pointed in some research. In this study, regarding the behavior of nervous system, which has non-linear dynamic behavior, we study the behavior of nervous system under exposure to magnetic field. We investigate whether the low frequency field is able to affect the dynamic of nerve cells and to have influence on non-linear features of signal. We used 6 environmental intensities and 6 cells have been used in each intensity, and by calculating some of non-linear features of action potential such as Higuchi Dimension and Return map of signal, during the time and in some different intensities of magnetic fields, It was observed that all intensities magnetic fields lead to increasing in Higuchi Dimension and increasing in the scattering of the Return map of signal. Of course these effects has been more observed in the middle band of frequency which has been confirmed by the theory of ‘frequency window’ effect of magnetic fields, which it has been noticed and discussed in last two decades.
Biomechanical Motor Control / Motor Control of Human Movement
Hamed Ghomashchi; Ali Esteki; Ali Motie Nasrabadi; Fereydoun Nowshiravan Rahatabad
Volume 4, Issue 3 , June 2010, , Pages 177-185
Abstract
In this study a simple inverted pendulum model with PID controller and delayed feedback is used to model standing-still postural control system for the purpose of achieving useful information about its underlying control structure. Using the Genetic algorithm and an experimental study results, the model ...
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In this study a simple inverted pendulum model with PID controller and delayed feedback is used to model standing-still postural control system for the purpose of achieving useful information about its underlying control structure. Using the Genetic algorithm and an experimental study results, the model and the controller parameters were estimated in a way that the model mimics real experimental sway patterns. The controller parameters found meaningful interpretations and it is shown that degeneration of postural control system affects the values of the parameters. Our findings indicate that although the simple models are not able to describe complexities of postural control system and interactions between its components, they can help us to improve our understanding of postural control system, its performance, its features and the way that the features change.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Sorour Behbahani; Ali Motie Nasrabadi
Volume 4, Issue 1 , June 2010, , Pages 53-64
Abstract
The analysis of EEG signals plays an important role in a wide range of applications, such as psychotropic drug research, sleep studies, seizure detection and hypnosis processing. From years ago hypnosis was known as a method to help the patients in different fields such as reduction of stress, leaving ...
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The analysis of EEG signals plays an important role in a wide range of applications, such as psychotropic drug research, sleep studies, seizure detection and hypnosis processing. From years ago hypnosis was known as a method to help the patients in different fields such as reduction of stress, leaving bad habits, pain control and etc. EEG signals during pure hypnosis would differ from those recorded in the normal no hypnotic conditions. There are several methods for analyzing the EEG signal and similarity index method is one of the famous methods in this branch. In this paper the features of EEG signal of three groups of people with different hypnotizability during hypnosis (Fractal, Wavelet Entropy, Frequency Bands) from left-right and frontal-back lobes were extracted and analyzed using Fuzzy Similarity Index Method to find whether there are any significant relations between the function of these hemispheres and hypnotizability degree. Finally after detecting the significancy, we used the selected features were used to classify the subjects into three groups of hypnotizability. The best classification accuracy was obtained 94% (for two classes of features 1. entropy, Higuchi, high frequency, 2. energy and entropy) and the lowest was 87.5% (for entropy, Higuchi and low frequency features).
Rehabilitation Engineering
Hamed Ghomashchi; Ali Esteki; Ali Motie Nasrabadi
Volume 2, Issue 2 , June 2008, , Pages 95-107
Abstract
In this study, the underlying dynamics of postural control system during quiet standing were investigated. Single-subject (SS) analysis was used as the statistical technique to compare the results. Center of pressure (COP) trajectories of 21 trials of a standing healthy subject and 24 trials of a cerebrovascular ...
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In this study, the underlying dynamics of postural control system during quiet standing were investigated. Single-subject (SS) analysis was used as the statistical technique to compare the results. Center of pressure (COP) trajectories of 21 trials of a standing healthy subject and 24 trials of a cerebrovascular attacked (CVA) patient were considered in our analysis. Complexity, dimensionality and stability of postural balance control system were evaluated using the first local minimum of auto mutual information (AMI) function, correlation dimension (Dc) and largest lyapunov exponent (LLE), respectively. The results indicated higher time delays (higher determinism), lower correlation dimension (lower active dynamical degrees of freedom) and lower LLE (increase of local stability) in the postural steadiness time series of the CVA patient in compare with the normal subject. The results showed that these measures not only can be used as pathologic measures to distinguish healthy subjects from CVA patient but also provide us new openings to disclose the postural control mechanism during a quiet standing.
