A5021255536- EEG and Brain-Computer Interfaces
- Functional Brain Connectivity Studies
- Neurological disorders and treatments
- Parkinson's Disease Mechanisms and Treatments
- Neural dynamics and brain function
- Blind Source Separation Techniques
- Advanced Neuroimaging Techniques and Applications
- Advanced MRI Techniques and Applications
- Muscle activation and electromyography studies
- Vestibular and auditory disorders
- Motor Control and Adaptation
- Transcranial Magnetic Stimulation Studies
- Neuroscience and Neural Engineering
- Balance, Gait, and Falls Prevention
- Botulinum Toxin and Related Neurological Disorders
- Tactile and Sensory Interactions
- Genetic Neurodegenerative Diseases
- Autism Spectrum Disorder Research
- Hearing, Cochlea, Tinnitus, Genetics
- Multiple Sclerosis Research Studies
- Stroke Rehabilitation and Recovery
- ECG Monitoring and Analysis
- Epilepsy research and treatment
- Gaze Tracking and Assistive Technology
- Visual perception and processing mechanisms
University of British Columbia
2016-2025
University of British Columbia Hospital
2005-2024
Vancouver Coastal Health
2016-2024
Faculty (United Kingdom)
2024
Canadian Sport Centre Pacific
2013-2023
Beijing Geriatric Hospital
2022
Shahab Danesh University
2022
Sharif University of Technology
2022
Institute for Cognitive Science Studies
2022
University of Oxford
2022
Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from recordings, especially those arising movements blinks. Often regression the time or frequency domain is performed on parallel electrooculographic (EOG) recordings derive parameters characterizing...
Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing measured signals. Here we describe new method for analyzing fMRI based on independent component analysis (ICA) algorithm Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight sets from 4 normal subjects performing Stroop color-naming, Brown Peterson work/number task, control tasks...
Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from recordings, especially those arising movements blinks. Often regression the time or frequency domain is performed on parallel electrooculographic (EOG) recordings derive parameters characterizing...
The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings is important both for basic brain research medical diagnosis treatment. Independent component (ICA) an effective method removing artifacts separating sources the signals from these recordings. A similar approach proving useful analyzing functional magnetic resonance imaging (fMRI) data. In this paper, we outline assumptions underlying ICA demonstrate its application to a variety electrical hemodynamic...
Independent component analysis (ICA), which separates fMRI data into spatially independent patterns of activity, has recently been shown to be a suitable method for exploratory analysis. The validity the assumptions ICA, mainly that underlying components are and add linearly, was explored with representative set by calculating log-likelihood observing each voxel's time course conditioned on ICA model. probability courses from white-matter voxels higher compared other observed brain regions....
A method is given for determining the time course and spatial extent of consistently transiently task-related activations from other physiological artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used analyze two fMRI data sets a subject performing 6-min trials composed alternating 40-sec Stroop color-naming control task blocks. Each consisted fixed three-dimensional distribution brain voxel values (a “map”) an associated...
Attention deficit hyperactivity disorder (ADHD) is one of the most common mental health disorders. As a neuro development disorder, neuroimaging technologies, such as magnetic resonance imaging (MRI), coupled with machine learning algorithms, are being increasingly explored biomarkers in ADHD. Among various methods, deep has demonstrated excellent performance on many tasks. With availability publically-available, large data sets for training purposes, learning-based automatic diagnosis...
Abstract Background The objective of this study was to examine the effects aerobic exercise on evoked dopamine release and activity ventral striatum using positron emission tomography functional magnetic resonance imaging in Parkinson's disease (PD). Methods Thirty‐five participants were randomly allocated a 36‐session or control intervention. Each participant underwent an scan while playing reward task before after intervention determine effect anticipation reward. A subset (n = 25)...
Many functional neuroimaging studies of biological motion have used as stimuli point-light displays walking figures and compared the resulting activations with those evoked by same display elements moving in a random or noncoherent manner. Although these established that activates superior temporal sulcus (STS), use controls has left open possibility coordinated meaningful nonbiological might activate brain regions thus call into question their specificity for processing motion. Here we...
Electroencephalography (EEG) recordings are often contaminated by muscle artifacts. In the literature, a number of methods have been proposed to deal with this problem. Yet most denoising artifact designed for either single-channel EEG or hospital-based, high-density multichannel recordings, not few-channel scenario seen in ambulatory instruments. paper, we propose utilizing interchannel dependence information situation combining multivariate empirical mode decomposition and canonical...
Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts. Muscular activities strongly obscure EEG signals and complicate subsequent EEG-based data analysis. Conventional methods for removing artifact from usually based on blind source separation techniques involve jointly analyzing multichannel recordings. Instead of using the approaches, this paper proposes to explore single-channel removal EEG. It may seem paradoxical that we denoise each channel individually...
Conventional blind source separation (BSS) methods have become widely adopted tools for neurophysiological data analysis. However, the increasing availability of multiset and multimodal has posed new challenges BSS originally designed to analyze one set at a time. Concomitantly, there is growing recognition that joint analysis potential substantially enhance our understanding brain function by extracting information from complementary modalities synergistically combining results. Therefore,...
Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on non-invasively measured brain activity. Traditional EEG methods have achieved moderate success when applied data acquired in static, well-controlled lab environments. However, an open-world environment is a more realistic setting, where situations affecting recordings can emerge unexpectedly, significantly weakening robustness existing methods. In recent years,...
Pervasive electroencephalographic (EEG) artifacts associated with blinks, eye-movements, muscle noise, cardiac signals, and line noise poses a major challenge for EEG interpretation analysis. Here, we propose generally applicable method removing wide variety of from records based on an extended version the independent component analysis (ICA) algorithm performing blind source separation linear mixtures signals. Our results show that ICA can effectively separate remove contamination artifact...
Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing measured signals. Here we describe new method for analyzing fMRI based on independent component analysis (ICA) algorithm Bell and Sejnowski ([1995]: Neural Comput 7:1129–1159). We decomposed eight sets from 4 normal subjects performing Stroop color-naming, Brown Peterson word/number task, control tasks...