Hello,
I’m a seasoned Biomedical Engineer with a strong focus on Biomechanics. I hold a M.Sc. from Sahand University of Technology and a B.Sc. from the Islamic Azad University Science and Research Branch, both in Iran. With a career spanning over a decade, I’ve honed my skills in various capacities including as a Technical Director and Researcher. My experience extends from working at Fanavaran Tose’e Tebe Mehr and Nasle Noandishane Pars, where I handled Zimmer Biomet T.K.A. & T.H.A. devices, to conducting research at Aryogen Pharmed and the Pasteur Institute of Iran.
I’ve also had the privilege of being a Visiting Scholar at The Beuth University of Applied Sciences Berlin. My collaborative nature has led me to be part of teams such as the Computational Neuroscience member at Neuromatch Academy and a Student Conference Volunteer at the IEEE Conference.
If you are interested to know more about me, I’ll be glad if you find it in my CV and my skills. more over, it is my pleasure if you contact me through Email as well.
Project
Decoding ECoG hand and tongue movement
Brain-Computer Interface (BCI) is a communication system that uses brain signals to control devices, being of high relevance for people suffering from severe paralysis, such as locked-in syndrome (LIS), that can be caused by e.g. stroke, metabolic diseases, neuromuscular disorders (such as ALS), cerebral palsy. A common BCI consists of (1) a training or calibration part, (2) a classifier setup, and (3) the online part, where users receive direct feedback. In this study we focus on building a classifier that decodes movements from electrocorticography (ECoG) derived cortical activity. We aim to identify which temporal, spatial and spectral features maximize the classification accuracy of actual and imagined movements per and across subjects. We will use part of the dataset from Miller 2019, which contains ECoG signals from 7 subjects who performed actual and imagined finger and tongue movements. The continuous ECoG was divided into epochs containing the actual (hand and tongue) and imagined (hand and tongue) movement. We will first start our investigation within each subject and then try to generalize our pipeline to analyze data across subjects. Our data analysis pipeline includes extracting the most relevant channels and slicing time window, PCA, and, finally, performing classification.
