TY  - THES
AB  - Monitoring cognitive effort is challenging due to complex neural networks and variability in task engagement. This study presents an electroencephalogram (EEG) and machine learning-based approach to estimate cognitive effort during listening tasks. We developed an EEG processing pipeline incorporating a novel method for separating ocular artifacts from cortical signals and evaluated multiple classification strategies. While alternative classifiers performed similarly to prior research, they highlighted limitations of current approaches. Extending EEG-based cognitive load estimation to a new population, we applied the system to individuals with aphasia and controls during naturalistic listening tasks of varying complexity. The system distinguished EEG data from difficult versus easy passages in both groups, though classification accuracy was lower than in previous studies, likely due to subtle task manipulations and inconsistent effort levels. These findings inform future refinements for cognitive monitoring in clinical populations.
AD  - Oregon Health and Science University
AU  - Quinn, Max
DA  - 2013
DO  - 10.6083/M4833Q22
DO  - DOI
ID  - 885
KW  - Machine Learning
KW  - Artificial Intelligence
KW  - Electroencephalography
KW  - Aphasia
KW  - Cognition
KW  - Signal Processing, Computer-Assisted
L1  - https://digitalcollections.ohsu.edu/record/885/files/888_etd.pdf
L2  - https://digitalcollections.ohsu.edu/record/885/files/888_etd.pdf
L4  - https://digitalcollections.ohsu.edu/record/885/files/888_etd.pdf
LK  - https://digitalcollections.ohsu.edu/record/885/files/888_etd.pdf
N2  - Monitoring cognitive effort is challenging due to complex neural networks and variability in task engagement. This study presents an electroencephalogram (EEG) and machine learning-based approach to estimate cognitive effort during listening tasks. We developed an EEG processing pipeline incorporating a novel method for separating ocular artifacts from cortical signals and evaluated multiple classification strategies. While alternative classifiers performed similarly to prior research, they highlighted limitations of current approaches. Extending EEG-based cognitive load estimation to a new population, we applied the system to individuals with aphasia and controls during naturalistic listening tasks of varying complexity. The system distinguished EEG data from difficult versus easy passages in both groups, though classification accuracy was lower than in previous studies, likely due to subtle task manipulations and inconsistent effort levels. These findings inform future refinements for cognitive monitoring in clinical populations.
PB  - Oregon Health and Science University
PY  - 2013
T1  - EEG-based cognitive load estimation
TI  - EEG-based cognitive load estimation
UR  - https://digitalcollections.ohsu.edu/record/885/files/888_etd.pdf
Y1  - 2013
ER  -