Week 5 (Jason Chang)
This week
has been one of many firsts – some firsts for my immersion experience and some
firsts simply for me. This past weekend, I went back home to Dallas to run a
half-marathon with my old professors and lab mates from the University of Texas
at Dallas. Despite having to run in the sweltering Texas heat, I was proud of
myself for sticking it out and successfully finishing my first (and hopefully
not last) race.
Figure 1. Post-race photo with (from left to right) my Feedback Systems
instructor, undergraduate PI, and a PhD student in my old lab
After returning
to NYC, I spent the first half of the week observing OR procedures for the
first time with Matthew and his clinical mentor, Dr. Jason Spector, in the Plastic
& Reconstructive Surgery Department at WCMC-NYP. I watched a total of five procedures,
including the excision of an unknown mass (which was later identified as calcified
fatty tissue), the placement of lumbar drain tubes to minimize post-operative swelling,
and a flap delay procedure of a patient’s pectoral skin in preparation for potential
reconstruction. While in the OR, Dr. Spector would ask the medical students questions
about the patients’ conditions and have them explain their answers to Matthew
and me, which I appreciated since I often got lost just watching the surgeons cleanly
cut and suture with incredible speed. Since the patients in the Neuroscience
ICU are all pre- and post-op, I am glad that I had this opportunity to see this
other side of medical treatment.
For my
research project under Dr. Mangat, I am also excited to report that I have analyzed
my first EEG dataset! Previously, I was having a lot of problems running my independent
component analysis (ICA) script with EEGLAB, an interactive EEG analysis
toolbox in MATLAB. Now, I have decided on a specific set of analysis parameters
to identify, mark, and remove EEG artifacts post-ICA processing of each
channel.
Figure 2. A selfie with my 2D scalp maps generated after initial EEG artifact
removal using independent component analysis (ICA)
ICA
analysis on one EEG dataset can take up to an hour, so I plan to keep running my
code over the weekend and next week. Although the processed EEG tracings are
now relatively clean, they still contain a considerable number of artifacts in
some channels. An additional ICA analysis may need to be applied to these ‘pruned’
datasets to better isolate the signals of interest. Afterwards, I plan to meet
with Dr. Mangat to go through the frequency spectra of my datasets to determine
whether the artifact removal was performed effectively.
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