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Date

12-8-2020 12:10 PM

Abstract

The purpose of this study is to clarify the uses of Individual Component Analysis (ICA) to process language related event-related potential (ERP) data. ICA is a method of time series analysis that is particularly powerful when analyzing multiple, non-Gaussian, signals at once, where each signal is independent of one another. Previous research has shown that the runica and binica via the EEGLAB toolbox methods are valid for this purpose, but it is unclear which method is most efficient for our current data with minimal data loss. The current dataset focuses on the N400, a linguistic component that indexes semantic processing. Both ICA methods are applied to the current dataset. Data loss is compared between binica and runica methods and will be analyzed to see how effectively the signal is kept intact while removing noise and participant error. Results will be used to determine which method to apply to future ERP research. A better understanding of the validity of the processing tools will contribute to future work regarding noisier language-related ERPs.

Biographies

Candice Bland Major: Mathematics
Candice Bland is an undergraduate at Portland State University. She is a Mathematics major and Physics minor. Candice is also an alumna of Portland Community College, where she first acquired her passion for learning. Slated to graduate with a Bachelor of Science in Winter 2021, Candice has developed an intense love of gravitational waves and climate science. Candice was a member of the Center for Climate and Aerosol Research under the advisement of Dr. Christopher Butenhoff, where she did computer modeling and simulation. After graduating from Portland State University, she will pursue a PhD in Physics to focus on gravitational waves. Candice has a fondness for black holes and yearns of working with LIGO to help advance interferometer technology after completing graduate school. In her spare time, Candice enjoys playing the ukulele and koto. She has strong beliefs that playing musical instruments makes her a better mathematician and physicist, due to the intrinsic connection between differential equations and string instruments. She feels that this highlights her approach to learning: that many different approaches are important for a well-rounded understanding of a topic, and that inspiration can be found anywhere.

Faculty Mentor: Dr. Sarah Key-DeLyria
Dr. Key-DeLyria's Neurolinguistics Lab in the Speech & Hearing Sciences Department examines how cognitive and linguistic processes work together to support communication in adults with and without traumatic brain injury history, aphasia, or other sources of cognitive impairments such as ADHD or learning disability.

Disciplines

Physical Sciences and Mathematics

Persistent Identifier

https://archives.pdx.edu/ds/psu/33585

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Aug 12th, 12:10 PM

The Use of Individual Component Analysis for N400 Events

The purpose of this study is to clarify the uses of Individual Component Analysis (ICA) to process language related event-related potential (ERP) data. ICA is a method of time series analysis that is particularly powerful when analyzing multiple, non-Gaussian, signals at once, where each signal is independent of one another. Previous research has shown that the runica and binica via the EEGLAB toolbox methods are valid for this purpose, but it is unclear which method is most efficient for our current data with minimal data loss. The current dataset focuses on the N400, a linguistic component that indexes semantic processing. Both ICA methods are applied to the current dataset. Data loss is compared between binica and runica methods and will be analyzed to see how effectively the signal is kept intact while removing noise and participant error. Results will be used to determine which method to apply to future ERP research. A better understanding of the validity of the processing tools will contribute to future work regarding noisier language-related ERPs.