Ph.D. Dissertation Defense: David Nahmias

Friday, April 3, 2020
10:00 a.m.
Zoom - (Audio and video enabled) meeting link: https://umd.zoom.us/j/487498449
Maria Hoo
301 405 3681
mch@umd.edu

ANNOUNCEMENT: Ph.D. Dissertation Defense  
 
 
Name: David Nahmias
 
Committee Members:
Professor Jonathan Simon, Chair and co-Advisor
Dr. Kimberly Kontson, co-Advisor
Professor Rama Chellappa
Professor Behtash Babadi
Professor Edward Bernat, Dean's Representative
 
Date: Friday, April 3rd, 2020 at 10:00 AM to 12:00 PM
 
Location: Zoom - (Audio and video enabled) meeting link: https://umd.zoom.us/j/487498449
 
 
Title: Advances in Quantitative Characterizations of Electrophysiological Neural Activity
 
Abstract:
 
Disorders of the brain and nervous system result in more hospitalizations and lost productivity than any other disease group. Electroencephalography (EEG), which measures brain electrical signals from the scalp, is a common neuro-monitoring technique used for diagnostic, rehabilitative, and therapeutic purposes. Understanding EEG quantitatively and its neural correlates with patient characteristics could inform the safety and efficacy of technologies that rely on EEG. In this dissertation, a large clinical data set comprised of over 35,000 recordings as well as data from previous research experiments are utilized to better quantify characteristics of neurological activity.

We first propose non-parametric methods of evaluating consistency of quantitative EEG features (qEEG) by applying novel statistical approaches. These results provide data-driven methods of identifying qEEG and their spatial characteristics ideal for various applications, and determining consistencies of novel features using existing data.

These qEEG are commonly used in feature-based machine learning applications. Further, EEG-driven deep learning has shown promising results in distinguishing recordings of subjects. To better understand the performance of these two machine learning approaches, we assess their ability to distinguish between subjects taking different anticonvulsants. Our methods could successfully discriminate between patients taking either anticonvulsant and those taking no medications solely from neural activity with similar performance from both feature-based and deep learning approaches.

With feature-based methods, it is easier to interpret which qEEG have the most impact on algorithm performance. However, deep learning applications in EEG can present difficulty in understanding and investigating underlying neurophysiological implications. We propose and validate a method to investigate frequency band importance in EEG-driven deep learning models. The easy perturbation EEG algorithm for spectral importance (easyPEASI) is simpler than previous methods and is applied to classifications investigated in this work.

Until this point, our work used well segmented EEG from clinical settings. However, EEG is usually corrupted by noise which can degrade its utility. We formulate and validate novel approaches to score electrophysiological signal quality based on the presence of noise from various sources. Further, we apply our method to compare and evaluate the performance of existing artifact removal algorithms.
 
David Nahmias is inviting you to a scheduled Zoom meeting.

Topic: David Nahmias - Dissertation Defense
Time: Apr 3, 2020 10:00 AM Eastern Time (US and Canada)

Join Zoom Meeting
https://umd.zoom.us/j/487498449

Meeting ID: 487 498 449

One tap mobile
+19294362866,,487498449# US (New York)
+13126266799,,487498449# US (Chicago)

Dial by your location
        +1 929 436 2866 US (New York)
        +1 312 626 6799 US (Chicago)
        +1 669 900 6833 US (San Jose)
        +1 253 215 8782 US
        +1 301 715 8592 US
        +1 346 248 7799 US (Houston)
Meeting ID: 487 498 449
Find your local number: https://umd.zoom.us/u/ad9650lzi 
 
 
 
 
 
 
 
 
 
 
 
 
   
 
 
 
 
 
 
 
 
 
 

Audience: Graduate  Faculty 

remind we with google calendar

 

September 2020

SU MO TU WE TH FR SA
30 31 1 2 3 4 5
6 7 8 9 10 11 12
13 14 15 16 17 18 19
20 21 22 23 24 25 26
27 28 29 30 1 2 3
Submit an Event