[4eyes] Practice Talk for Thesis Defense

[Dianna Han]

Hi everyone,

I have my defense scheduled on Tuesday, Oct. 3rd. I'm hoping to have a
practice talk for the lab members next week before the formal presentation.
I've attached the abstract of my thesis below; if you are interested,
please let me know what time you prefer - I'm thinking about 9/20
(Thursday) or 9/21 (Friday), most likely a time in the early afternoon. I
really appreciate your help and look forward to your feedback on the talk.

Thank you!

Regards,
Dianna

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Abstract:

Recent technology has provided many tools and methods that allow
researchers to look further into the blackbox of human minds. These include
electroencephalogram (EEG), magnetoencephalography (MEG), positron emission
tomography (PET), and magnetic resonance imaging (MRI), among which EEG and
functional MRI (fMRI), two non-invasive techniques, dominate the field.
These two major instruments have enabled researchers to identify latent
neural processes and decode certain important cognitive states. Following
the recent advances in neuroimaging technology that enable the concurrent
recording of EEG and fMRI, much effort has been made to integrate these two
modalities in order to leverage their complementary powers, or identify and
characterize the underlying mechanism of neurovascular coupling. Numerous
controlled experiments have been carefully designed and carried out, and
large volumes of data have been recorded. Multiple statistical methods,
including machine learning, have been applied to interpret these data, or
to mine the corpus of information they create.


In this dissertation, we continue in this line of study, and propose
methods built upon advanced statistical tools: HMM- and RNN-based methods
for modeling and classifying cognitive states, and a temporal extension of
canonical correlation analysis (CCA) for identifying temporal correlations
between different modalities. We chose to tackle the complicated problem of
decoding neural processes through two very different experimental data
sets. In one study, fMRI data were collected when subjects were exposed to
video/audio cues to induce craving of cigarettes; in the other study, both
EEG and fMRI data were collected simultaneously when subjects were
presented with visual stimuli in a spatial working memory task. Dynamic
models that capture the temporal patterns in the data and exploratory
methods that reveal the underlying relationship between modalities were
designed and tested over the data sets. The findings and reflections of
these studies are described here. Our work is one step closer to the goal
of unlocking the secrets of human minds.
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