[4eyes] Practice Talk for Thesis Defense

[Matthew Turk]

We will not have a lab meeting this week, so it would be nice if many of you would give Dianna feedback on her practice talk during the regular lab meeting time. You can also learn something about the use of fMRI, EEG, and machine learning in neuroscience!

 

                Matthew

 

From: Ilab-users [mailto:ilab-users-bounces at lists.cs.ucsb.edu] On Behalf Of Dianna Han
Sent: Tuesday, September 19, 2017 10:18 AM
To: ilab-users at lists.cs.ucsb.edu
Subject: Re: [4eyes] Practice Talk for Thesis Defense

 

Hi everyone,

 

Thank you for replying to my previous emails despite the unfortunate issue with my email account!

 

We have reserved 2-3:30pm on Sep. 22nd (Friday) at 2003 Elings (the regular lab meeting time & location). Everyone is welcome, and light refreshments will be provided. I look forward to your feedback.

 

Regards,

Dianna

 

 

On Fri, Sep 15, 2017 at 12:57 PM, Dianna Han <dianna.han at gmail.com <mailto:dianna.han at gmail.com> > wrote:

Hi everyone,

 

I'm so sorry for the inconvenience - something is wrong with my cs email account.

 

Please reply to my gmail account here if you are interested in my practice talk. Thanks!

 

Best,

Dianna

 

---------- Forwarded message ---------
From: Dianna Han <dianna at cs.ucsb.edu <mailto:dianna at cs.ucsb.edu> >
Date: Fri, Sep 15, 2017 at 11:45 AM
Subject: Practice Talk for Thesis Defense
To: <ilab-users at lists.cs.ucsb.edu <mailto:ilab-users at lists.cs.ucsb.edu> >

 

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

 

------------

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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