Search for: electroencephalogram
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    Cognitive memory comparison between tinnitus and normal cases using event-related potentials

    , Article Frontiers in Integrative Neuroscience ; Volume 12 , 2018 ; 16625145 (ISSN) Asadpour, A ; Alavi, A ; Jahed, M ; Mahmoudian, S ; Sharif University of Technology
    Frontiers Media S.A  2018
    About 20 percent of people above 60 years old suffer from tinnitus though no definitive treatment has been found for it. Evaluation of electrical brain activity using Event-Related Potentials (ERPs) is one of the methods to investigate the underlying reasons of tinnitus perception. Previous studies using ERPs suggest that the precognitive memory in tinnitus groups is negatively affected in comparison to the normal hearing groups. In this study, cognitive memory has been assessed using visual and auditory P300 response with oddball paradigm. Fifteen chronic tinnitus subjects and six normal hearing subjects participated in the experiment. T-test with significance level of 0.05 was applied on... 

    Brain waves evaluation of sound therapy in chronic subjective tinnitus cases using wavelet decomposition

    , Article Frontiers in Integrative Neuroscience ; Volume 12 , 2018 ; 16625145 (ISSN) Asadpour, A ; Jahed, M ; Mahmoudian, S ; Sharif University of Technology
    Frontiers Media S.A  2018
    Management and treatment of subjective tinnitus is an ongoing focus of research activities. One of the most viable assessments of such treatment is the evaluation of brain activity in addition to patient response and clinical assessment. This study focuses on sound therapy and evaluation of patients’ electroencephalogram (EEG) in order to verify the potency of this approach. Broadband sound therapy was applied to nineteen participants aging from 25 to 64 and suffering from chronic subjective tinnitus to study the difference of brain activity, a) before fake treatment, b) after fake treatment and c) after the main treatment, using EEG and Visual Analog Scale (VAS) for evaluating Residual... 

    Epileptic seizure detection based on video and EEG recordings

    , Article 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings, 19 October 2017 ; Volume 2018-January , 2018 , Pages 1-4 ; 9781509058037 (ISBN) Aghaei, H ; Kiani, M. M ; Aghajan, H ; IEEE Circuits and Systems Society (CAS); IEEE Engineering in Medicine and Biology Society (EMBS); SSCS ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    Clinical data from epileptic patients reveal important information about the characteristics of the particular type of epilepsy. Such data is often acquired in a bimodal fashion, e.g. video recordings are collected with the standard Electroencephalogram (EEG) data, in order to help the specialists validate their assessment based on one modality with the other. Manual annotation of the onset of seizures across several days' worth of data is time consuming. This paper proposes an automated epilepsy seizure detection method based on a combination of features from EEG and video data, and compares it against detectors using either modality alone. © 2017 IEEE  

    Aberrant frequency related change-detection activity in chronic tinnitus

    , Article Frontiers in Neuroscience ; Volume 14 , 2020 Asadpour, A ; Jahed, M ; Mahmoudian, S ; Sharif University of Technology
    Frontiers Media S.A  2020
    Tinnitus is the perception of sound without the occurrence of an acoustic event. The deficit in auditory sensory or echoic memory may be the cause of the perception of tinnitus. This study considered the mismatch negativity (MMN) to investigate the potential difference between and within groups of persons with normal hearing (NH) and tinnitus. Using an auditory multi-feature paradigm to elicit the MMN, this study considered the MMN peak amplitude at two central frequencies for two MMN subcomponents. These central frequencies were 1 and 5 kHz, which the latter was closer to the perceived tinnitus frequency in the group with tinnitus. The deviants were higher frequency, lower frequency, higher... 

    Estimating the depth of anesthesia using fuzzy soft computation applied to EEG features

    , Article Intelligent Data Analysis ; Volume 12, Issue 4 , 2008 , Pages 393-407 ; 1088467X (ISSN) Esmaeili, V ; Assareh, A ; Shamsollahi, M. B ; Moradi, M. H ; Arefian, N. M ; Sharif University of Technology
    IOS Press  2008
    Estimating the depth of anesthesia (DOA) is still a challenging area in anesthesia research. The objective of this study was to design a fuzzy rule based system which integrates electroencephalogram (EEG) features to quantitatively estimate the DOA. The proposed method is based on the analysis of single-channel EEG using frequency and time domain methods. A clinical study was conducted on 22 patients to construct subsets of reference data corresponding to four well-defined anesthetic states: awake, moderate anesthesia, surgical anesthesia and isoelectric. Statistical analysis of features was used to design input membership functions (MFs). The input space was partitioned with respect to the... 

    EEG-based Personalized Interpretable Visual Attention Prediction

    , M.Sc. Thesis Sharif University of Technology Behnamnia, Armin (Author) ; Rabiee, Hamid Reza (Supervisor)
    Human visual attention is a mapping that determines to what regions of an image human’s eyes focus more while perceiving it. Personalized visual attention is visual attention computed for a specific individual. The importance of visual attention lies in its wide range of applications in computer vision and cognitive science, such as neural encoding, image captioning, self-driving cars, video anomaly detection, image classification, and visual design. One of important aspects of visual attention is personalization, the ability to assign every individual their own, specialized attention map. In this project we aim to utilize EEG signals measured from people’s brain to predict their... 

    A transfer learning algorithm based on linear regression for between-subject classification of EEG data

    , Article 25th International Computer Conference, Computer Society of Iran, CSICC 2020, 1 January 2020 through 2 January 2020 ; 2020 Samiee, N ; Sardouie, S. H ; Foroughmand Aarabi, M. H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    Classification is the most important part of brain-computer interface (BCI) systems. Because the neural activities of different individuals are not identical, using the ordinary methods of subject-dependent classification, does not lead to high accuracy in betweensubject classification problems. As a result, in this study, we propose a novel method for classification that performs well in between-subject classification. In the proposed method, at first, the subject-dependent classifiers obtained from the train subjects are applied to the test trials to obtain a set of scores and labels for the trials. Using these scores and the real labels of the labeled test trials, linear regression is... 

    Selection of efficient features for discrimination of hand movements from MEG using a BCI competition IV data set

    , Article Frontiers in Neuroscience ; Issue APR , 2012 ; 16624548 (ISSN) Sardouie, S. H ; Shamsollahi, M. B ; Sharif University of Technology
    The aim of a brain-computer interface (BCI) system is to establish a new communication system that translates human intentions, reflected by measures of brain signals such as magnetoencephalogram (MEG), into a control signal for an output device. In this paper, an algorithm is proposed for discriminating MEG signals, which were recorded during hand movements in four directions. These signals were presented as data set 3 of BCI competition IV. The proposed algorithm has four main stages: pre-processing, primary feature extraction, the selection of efficient features, and classification. The classification stage was a combination of linear SVM and linear discriminant analysis classifiers. The... 

    Alterations of the electroencephalogram sub-bands amplitude during focal seizures in the pilocarpine model of epilepsy

    , Article Physiology and Pharmacology ; Volume 16, Issue 1 , 2012 , Pages 11-20 ; 17350581 (ISSN) Motaghi, S ; Niknazar, M ; Sayyah, M ; babapour, V ; Vahdat, B. V ; Shamsollahi, M. B ; Sharif University of Technology
    Introduction: Temporal lobe epilepsy (TLE) is the most common and drug resistant epilepsy in adults. Due to behavioral, morphologic and electrographic similarities, pilocarpine model of epilepsy best resembles TLE. This study was aimed at determination of the changes in electroencephalogram (EEG) sub-bands amplitude during focal seizures in the pilocarpine model of epilepsy. Analysis of these changes might help detection of a pre-seizure state before an oncoming seizure. Methods: Rats were treated by scopolamine (1mg/kg, s.c) to prevent cholinergic effects. After 30 min, pilocarpine (380 mg/kg, i.p) was administered to induce status epilepticus (SE) and 2 hours after SE, diazepam (20 mg/kg,... 

    Brain Connectivity Analysis from EEG Signals using Entropy based Measures

    , M.Sc. Thesis Sharif University of Technology Saboksayr, Saman (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Even in the simplest of activities in the brain such as resting condition, there are connections in between different regions of the brain so that the whole system functions consistently in harmony. Studies related to brain connectivity provides an opportunity to better understand how the brain works. To assess these connectivities an estimation is usually conducted based on brain signals. Among different estimation methods, quantities of information theory are in general more practical due to avoiding any assumptions toward the system model and the ability to recognize linear and non-linear connectivity. One of the main quantities related to the information theory is in fact, entropy.... 

    Seizure Detection in Generalized and Focal Seizure from EEG Signals

    , M.Sc. Thesis Sharif University of Technology Mozafari, Mohsen (Author) ; Hajipour, Sepideh (Supervisor)
    Epilepsy is one of the diseases that affects the quality of life of epileptic patients. Epileptic patients lose control during epileptic seizures and are more likely to face problems. Designing and creating a seizure detection system can reduce casualties from epileptic attacks. In this study, we present an automatic method that reduces the artifact from the raw signals, and then classifies the seizure and non-seizure epochs. At all stages, it is assumed that no information is available about the patient and this detection is made only based on the information of other patients. The data from this study were recorded in Temple Hospital and the recording conditions were not controlled, so... 

    Study of Brain Oddball Response to Olfactory Stimuli as Indicator in Dementia Disorders

    , M.Sc. Thesis Sharif University of Technology Sedghizadeh, Mohammad Javad (Author) ; Karbalaee Aghajan, Hamid (Supervisor)
    High-frequency oscillations of the frontal cortex are involved in functions of the brain that fuse processed data from different sensory modules or bind them with elements stored in the memory. These oscillations also provide inhibitory connections to neural circuits that perform lower-level processes. Deficit in the performance of these oscillations has been examined as a marker for Alzheimer’s disease (AD). Additionally, the neurodegenerative processes associated with AD, such as the deposition of amyloid-beta plaques, do not occur in a spatially homogeneous fashion and progress more prominently in the medial temporal lobe in the early stages of the disease. This region of the brain... 

    Trial-by-trial surprise-decoding model for visual and auditory binary oddball tasks

    , Article NeuroImage ; Volume 196 , 2019 , Pages 302-317 ; 10538119 (ISSN) Modirshanechi, A ; Kiani, M. M ; Aghajan, H ; Sharif University of Technology
    Academic Press Inc  2019
    Having to survive in a continuously changing environment has driven the human brain to actively predict the future state of its surroundings. Oddball tasks are specific types of experiments in which this nature of the human brain is studied. Detailed mathematical models have been constructed to explain the brain's perception in these tasks. These models consider a subject as an ideal observer who abstracts a hypothesis from the previous stimuli, and estimates its hyper-parameters - in order to make the next prediction. The corresponding prediction error is assumed to manifest the subjective surprise of the brain. While the approach of earlier works to this problem has been to suggest an... 

    Interview based connectivity analysis of EEG in order to detect deception

    , Article Medical Hypotheses ; Volume 136 , 2020 Daneshi Kohan, M ; Motie NasrAbadi, A ; sharifi, A ; Bagher Shamsollahi, M ; Sharif University of Technology
    Churchill Livingstone  2020
    Deception is mentioned as an expression or action which hides the truth and deception detection as a concept to uncover the truth. In this research, a connectivity analysis of Electro Encephalography study is presented regarding cognitive processes of an instructed liar/truth-teller about identity during an interview. In this survey, connectivity analysis is applied because it can provide unique information about brain activity patterns of lying and interaction among brain regions. The novelty of this paper lies in applying an open-ended questions interview protocol during EEG recording. We recruited 40 healthy participants to record EEG signal during the interview. For each subject,... 

    Model-based Bayesian filtering of cardiac contaminants from biomedical recordings

    , Article Physiological Measurement ; Volume 29, Issue 5 , 2008 , Pages 595-613 ; 09673334 (ISSN) Sameni, R ; Shamsollahi, M. B ; Jutten, C ; Sharif University of Technology
    Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources of noise for other biomedical signals. In some recent works, a Bayesian filtering framework has been proposed for denoising the ECG signals. In this paper, it is shown that this framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such as the electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG. The proposed method is evaluated on simulated and real signals. © 2008 Institute of Physics and Engineering in Medicine  

    Analysis of Brain Signals in Response to Transcranial Magnetic Stimulation of Normal Subjects and Subjects with Tinnitus Disorder and Evaluation of its Therapeutic Effect

    , M.Sc. Thesis Sharif University of Technology Sheibani Asl, Nasrin (Author) ; Jahed, Mehran (Supervisor) ; Mahmoudian, Saeed (Co-Supervisor)
    According to clinical surveys, it is estimated that chronic tinnitus is experienced by 10 to 15% of the adult population. Tinnitus is defined as the conscious and involuntary perception of noise or ringing without involvement of either mechanical or external audio source. In recent years, a limited number of studies have investigated the effects of applying transcranial magnetic stimulation (TMS) to ameliorate this condition. TMS is a noninvasive intervention in which magnetic pulses are applied by a coil adjacent to the individual's head. In this study, electroencephalogram (EEG) signals are recorded concurrent and as a follow-up to TMS. This makes it possible to study changes caused by... 

    Temporal Analysis of Functional Brain Connectivity Using EEG Signals

    , M.Sc. Thesis Sharif University of Technology Khazaei, Ensieh (Author) ; Mohammadzadeh, Narges Hoda (Supervisor)
    Human has different emotions such as happiness, sadness, anger, etc. Recognizing these emotions plays an important role in human-machine interface. Emotion recognition can be divided into approaches, physiological and non-physiological signals. Non-physiological signals include facial expressions, body gesture, and voice, and physiological signals include electroencephalograph (EEG), electrocardiograph (ECG), and functional magnetic resonance imaging (fMRI). EEG signal has been absorbed a lot of attention in emotion recognition because recording of EEG signal is easy and it is non-invasive. Analysis of connectivity and interaction between different areas of the brain can provide useful... 

    Command Increasing in SSVEP based BCI Using Color

    , M.Sc. Thesis Sharif University of Technology Aghamiri Barzi, Alireza (Author) ; Shamsollahi, Mohamad Bagher (Supervisor)
    Nowadays, a brain-computer interface (BCI) systems is considered as one of the necessary needs for individuals who are capable of doing their daily tasks. Despite of recent technological developments, such systems are not widely utilized. Spelling vocabularies and controlling wheelchairs are the most useful examples of using BCI. Todays, there has been a wide range of methods for establishing the connection between mind and computer. One of these methods is brain -Computer Interface Applications based on Steady State Visual Evoked Potentials (SSVEP). Although, these applications has a capability of fast responds and data transitions, only a few tasks can be given to the computer. In this... 

    Investigation of Brain Connectivity Changes during Seizure using Graph Theory

    , M.Sc. Thesis Sharif University of Technology Khoshkhah Tinat, Atefeh (Author) ; Karbalai Aghajan, Hamid (Supervisor) ; Mohammadzadeh, Hoda (Co-Supervisor)
    Epilepsy is a chronic neurological disorder characterized by recurrent and abrupt seizures. Seizures occur due to disturbances in the interactions between the distributed neuronal populations in the brain. Investigation of the brain functional connectivity networks is a way to better understand how the brain functions during seizure. To estimate the brain functional connectivity network, we need criteria that can estimate the functional connections between the brain regions from the recorded brain functional data such as electroencephalogram (EEG) signals. After estimating the functional brain connectivity networks, it is possible to create graphs corresponding to these estimated networks... 

    Automatic ocular correction in EEG recordings using maximum likelihood estimation

    , Article IEEE International Symposium on Signal Processing and Information Technology, IEEE ISSPIT 2013, Athens ; 2013 , Pages 164-169 Karimi, S ; Molaee Ardekani, B ; Shamsollahi, M. B ; Leroy, C ; Derambure, P ; Sharif University of Technology
    IEEE Computer Society  2013
    The electrooculogram (EOG) artifact is one of the main contaminators of electroencephalographic recording (EEG). EOG can make serious problems in results and interpretations of EEG processing. Rejecting contaminated EEG segments result in an unacceptable data loss. Many methods were proposed to correct EOG artifact mainly based on regression and blind source separation (BSS). In this study, we proposed an automatic correction method based on maximum likelihood estimation. The proposed method was applied to our simulated data (real artifact free EEG plus controlled EOG) and results show that this method gives superior performance to Schlögl and SOBI methods