I have done research in the realms of: computational and cognitive neuroscience, brain computer interface (BCI), artificial intelligence (AI)/machine learning (ML) and user experience (UX). This research has been applied to several different projects I took on during my graduate studies, while working at Scotiabank to help predict customer risk and in building a better user experience for my start-up.
Table of Contents
(a) Additional Training in Computational Neuroscience
(b) Graph Theory and my introduction to Neuroscience
(c) TUTORIALS for pretty much ANY type of EEG analysis!!
(d) Some of my work
(a) Additional Training in Computational Neuroscience*
*Only covers neuroscience training excluded from my direct graduate or undergraduate studies
Computation and Cognitive Neuroscience Summer School (CCNSS) in Suzhou, China
I was selected as one of 32 students from 14 different countries. The CCNSS program trains PhD students in research domains associated with Computational Neuroscience with several world-renowned researchers over the course of several weeks. All programming is done in python. I got in on a full scholarship with all living expenses (hotel & food) covered by funding from google DeepMind
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ERP Bootcamp with lead expert Dr. Steven Luck in Vancouver, BC
Dr. Steven Luck is one of the lead experts in EEG ERP analysis. I attended one of his week-long bootcamps in which him and his colleagues provided extensive training on ERPs. It was a very thorough course in which they were available for help afterwards to sort out any questions that we had.
BESA workshop in Munich, Germany
BESA stands for Brain Electrical Source Analysis is a popular EEG software that is purchased for easy analysis in some labs. I attended the workshop in Germany in which training in advanced source imaging, source connectivity, and cluster permutation statistics was provided. We were taught to do fMRI co-registration and several forms of EEG source reconstruction. This was helpful in understanding similarities and differences between outputs of BESA and open source EEG analysis programs in python and matlab.
(b) Graph Theory and my introduction to Neuroscience
I absolutely LOVE neuroscience. It allows me to apply my computational background to countless undiscovered problems. My undergraduate thesis was in the math department investigating the use of a graph theory matrix (the combinatorial laplacian) in making deductions about large datasets. I have since used graph theory to investigated brain alterations in:
individuals following concussion
cerebral palsy patients who undergo power mobility treatment with Dr. Lisa Kenyon
breast cancer patients following chemotherapy with Dr. Kristin Campbell and Dr. Todd Handy.
in eating disorder patients when observing bodies of different sizes with Dr. Birmingham
I have also set up a system to synchronize 2 EEGs (to 1/1000th of a second) so that we can test between-brain coupling in my previous lab. We conducted two separate studies for this:
Exploring the differences in cooperative, competitive and individual tasks and;
Exploring the joint experience of ostracism with either common vs. divergent mindsets.
To see other types of EEG analysis and tutorials for each keep scrolling to section (c)
My husband and I trying out the between-brain scanning system (hyperscanning) that I set up a few days before our wedding. Were we synchronized?
Two participants about to start the hyperscanning game I set up.
(c) Tutorials for pretty much ANY type of EEG analysis
**(NOTE THAT SOME TUTORIAL PAGES WILL BE UPDTED BETWEEN FEB 10th AND MARCH 10th)
Other than graph theory I have applied pretty much any type of EEG analysis you could think of on my data:
To begin, start with preprocessing
ICA/PCA and other dimensionality reduction techniques
Time Frequency Analysis (being updated)
Cross frequency coupling (being updated)
Source analysis in Fieldtrip, python, brainstorm or BESA
Phase locking and power spectra (being updated)
