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15
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Introduction
Mohammad-Javad is a 1st-year Ph.D. student in biomedical engineering, University of Montreal. currently, he is working on the classification of affective states using deep learning approaches. Always he is looking for new challenges in data mining and Human-Computer Interface (BCI) technology. He has worked as a Signal Processing Engineer at National Brain Mapping Laboratory (NBML). He completed his master’s degree in Biomedical Engineering (2017) and holds a Bachelor of Science in Electrical Engineering (2014). Mohammad's research interests are Artificial Intelligence, especially its application in medicine and neurotechnology. He is passionate about innovation in AI and Neurotechnology.
Current institution
Additional affiliations
January 2019 - March 2023
January 2019 - March 2023
September 2014 - November 2016
Publications
Publications (15)
Introduction: Transcranial Direct Current Stimulation (tDCS) has been used as a non-invasive method to increase the plasticity of brain. Growing evidence has shown several brain disorders such as depression, anxiety disorders, and chronic pain syndrome are improved following tDCS. In patients with Obsessive-Compulsive Disorder (OCD), increased brai...
Machine learning models often fail to generalize well under distributional shifts. Understanding and overcoming these failures have led to a research field of Out-of-Distribution (OOD) generalization. Despite being extensively studied for static computer vision tasks, OOD generalization has been underexplored for time series tasks. To shine light o...
Aviation safety depends on the skill and expertise of pilots to meet the task demands of flying an aircraft in an effective and efficient manner. During flight training, students may respond differently to imposed task demands based on individual differences in capacity, physiological arousal, and effort. To ensure that pilots achieve a common desi...
Human memory retrieval is one of the brain's most important, and least understood cognitive mechanisms. Traditionally, research on this aspect of memory has focused on the contributions of particular brain regions to recognition responses, but the interaction between regions may be of even greater importance to a full understanding. In this study,...
Machine learning models often struggle with distribution shifts in real-world scenarios, whereas humans exhibit robust adaptation. Models that better align with human perception may achieve higher out-of-distribution generalization. In this study, we investigate how various characteristics of large-scale computer vision models influence their align...
Early detection and diagnosis of pathology are essential for efficient treatment and therapeutic interventions. The emergence of Artificial Intelligence (AI) and deep machine learning techniques have demonstrated the promising capability of brain imaging data to predict various pathological diseases. However, plenty of diseases have unbalanced dist...
Early detection and diagnosis of pathology are essential for efficient treatment and therapeutic interventions. The emergence of Artificial Intelligence (AI) and deep machine learning techniques have demonstrated the promising capability of brain imaging data to predict various pathological diseases. However, plenty of diseases have imbalanced dist...
Rehabilitative exercise for people suffering from upper limb impairments has the potential to improve their neuro-plasticity due to repetitive training. Our study investigates the usefulness of Electroencephalogram and Electromyogram (EMG) signals for incorporation in humanrobot interaction loop. Twenty healthy participants recruited who performed...
Simple N back presentation with Psychtoolbox in Matlab (Variation of position and colour in 9*9 square)
This code Presents N_Back stimulus
In this task colour and location of squares are changed, and the right/left responses save as a *.xls file that contains:
'SubID\ SubAge\ Gender\ Type\ i_back\ Time\ Reaction_Time\ Location\ Color or Pressed Key...
In the case that you need to estimate the mean of power spectral density in different frequency bands (Delta, Theta and...) for a specific channel or all channels. The EEGLAB GUI only gives you the plot However you need to have the values for downstream analysis( e.g. SPSS). This code is a way to get this data (mean value) exported.
This code is in...
More info and registeration at:
http://www.nbml.ir/fa/workshops/96/MATLAB-for-Neuroscience
Questions
Questions (13)
As far as I know, the basic assumptions of independent component analysis (ICA) are:
1) Mixing is linear
2) Propagation delays are negligible
3) Component time courses are independent
4) Number of components ≤ number of channels.
These assumptions hold in EEG data, but it has been shown the assumptions number 2 and 3 do not hold in fMRI data:
- Calhoun, V. D., Adali, T., Pekar, J. J., & Pearlson, G. D. (2003). Latency (in) sensitive ICA: group independent component analysis of fMRI data in the temporal frequency domain. NeuroImage, 20(3), 1661-1669.
- Tong, Y., Hocke, L. M., Fan, X., Janes, A. C., & Frederick, B. D. (2015). Can apparent resting state connectivity arise from systemic fluctuations?. Frontiers in human neuroscience, 9, 285.
It seems many fMRI studies use MELODIC [1].
My questions are that does this software consider or address this problem? How do studies that use this software address/relax this problem?
[1] Beckmann, C. F., & Smith, S. M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE transactions on medical imaging, 23(2), 137-152.
Dear all
In the eeglab when we plot channel time-frequency, we see blue color is higher than red (see attached picture)
How can I solve the color map problem in the eeglab?
bests,
Are this peaks related to noise? (In EEG spectral)
This is a data recorded simultaneously with eye tracker and in some subject, I see this pattern and I guess it is related to eye tracker noise. Anybody can guess what is the cause of this peaks? and how can I clean my signals?
We want investigate this from the cognitive neuroscience perspective can we call it neuromusic. Anybody here have an experience in this research field? I am seeking for good and ideal reference for this topic.
Have you any ideas?
What feature in EEG Signal will be important?
I am seeking for the best signal processing package or course in python, especially for EEG/MEG signal processing, what packages are available? and which is the best one?
I know that 50 100 and 150 Hz is line noise but what is the probable cause of other peaks?
I always used Matlab for signal processing but I see one of other strong recommendation is Python. always individuals use program language such as Matlab/python. why one may choose python for signal processing especially biomedical signal processing?
please read this:
How can we use EEG source localization in a clinical application?
Do you know best software and package? free or other conditions
Do you have experience with BESA or Epilog?
Dear all
After we acquisition EEG signal, How can ensure that the Data comply with relevant quality standards? and is reliable? And similar to it when we process a signal how can ensure that we correctly and completely do it? And on the other hand, didn't we remove Intrinsic property of data?
Also, After they record EEG/fMRI simultaneously, How perform QC?
assume we have two signal
1) signal isolated condition
2) signal that recorded In the presence of known noise
we pre-process 2nd signal and want to check our method. how can we do it?
