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electroencephalogram--eeg
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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) ; Assareh, A ; Shamsollahi, M. B ; Moradi, M. H ; Arefian, N. M ; Sharif University of Technology
IOS Press
2008
Abstract
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...
Study of Brain Oddball Response to Olfactory Stimuli as Indicator in Dementia Disorders
, M.Sc. Thesis Sharif University of Technology ; Karbalaee Aghajan, Hamid (Supervisor)
Abstract
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...
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) ; 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
Abstract
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
EEG-based Personalized Interpretable Visual Attention Prediction
, M.Sc. Thesis Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor)
Abstract
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 ; Sardouie, S. H ; Foroughmand Aarabi, M. H ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
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...
Resting-State electroencephalogram (EEG) coherence over frontal regions in paranormal beliefs
, Article Basic and Clinical Neuroscience ; Volume 13, Issue 4 , 2022 , Pages 573-584 ; 2008126X (ISSN) ; Hatami, J ; Khosrowabadi, R ; Sohrabi, A ; Sharif University of Technology
Iran University of Medical Sciences
2022
Abstract
Introduction: Paranormal beliefs are defined as the belief in extrasensory perception, precognition, witchcraft, and telekinesis, magical thinking, psychokinesis, superstitions. Previous studies corroborate that executive brain functions underpin paranormal beliefs. To test this hypotheses, neurophysiological studies of brain activity are required. Methods: A sample of 20 students (10 girls, Mean±SD age: 22.50±4.07 years) were included in the current study. The absolute power of resting-state electroencephalogram (EEG) was analyzed in intra-hemispheric and inter-hemispheric coherence with eyes open. The paranormal beliefs were determined based on the total score of the revised paranormal...
Denoising of ictal EEG data using semi-blind source separation methods based on time-frequency priors
, Article IEEE Journal of Biomedical and Health Informatics ; Volume 19, Issue 3 , July , 2015 , Pages 839-847 ; 21682194 (ISSN) ; Shamsollahi, M. B ; Albera, L ; Merlet, I ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2015
Abstract
Removing muscle activity from ictal ElectroEncephaloGram (EEG) data is an essential preprocessing step in diagnosis and study of epileptic disorders. Indeed, at the very beginning of seizures, ictal EEG has a low amplitude and its morphology in the time domain is quite similar to muscular activity. Contrary to the time domain, ictal signals have specific characteristics in the time-frequency domain. In this paper, we use the time-frequency signature of ictal discharges as a priori information on the sources of interest. To extract the time-frequency signature of ictal sources, we use the Canonical Correlation Analysis (CCA) method. Then, we propose two time-frequency based semi-blind source...
EEG based biometrics using emotional stimulation data
, Article 5th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2017, 21 December 2017 through 23 December 2017 ; Volume 2018-January , February , 2018 , Pages 246-249 ; 9781538621752 (ISBN) ; Arasteh, A ; Sarkar, A. K ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
EEG based biometrics using linear Support Vector Machine (SVM) is proposed in this paper. Human identification using electroencephalographic signal was done in this research. Reliability of most of the biometrics systems is not up to the mark because of the possibility of being faked or duplicated. Here, the brain signatures were used as a possible biometric identifier. A Database for Emotion Analysis using Physiological Signals containing 40 trials from each participant was used. International 10-20 system of EEG electrode placement was employed and data from Cz electrode was taken for this research. Some researches showed nice performance with few subjects. Here, 20 subjects were used from...
A transfer learning algorithm based on csp regularizations of recorded eeg for between-subject classiftcation
, Article 26th National and 4th International Iranian Conference on Biomedical Engineering, ICBME 2019, 27 November 2019 through 28 November 2019 ; 2019 , Pages 199-203 ; 9781728156637 (ISBN) ; Hajipour Sardouie, S ; Mohammad, H ; Foroughmand Aarabi ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
Feature extraction and classification are the most important parts of BCI systems. The new branch of BCI studies focuses on the design of a classifier that is trained to function properly for each individual. This problem is known as Transfer Learning. In between-subject classification, due to the differences in the neural signals' distribution of different individuals, using the common methods of feature extraction for training the classifier, does not lead to high accuracy for the test subject. As a result, in this study, we present a method for extracting features that perform well in between subjects classifications. The data that we used in this study are EEG signals recorded during...