@article{ETD,
      recid = {885},
      author = {Quinn, Max},
      title = {EEG-based cognitive load estimation},
      publisher = {Oregon Health and Science University},
      school = {M.S.},
      address = {2013},
      number = {ETD},
      abstract = {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.},
      url = {http://digitalcollections.ohsu.edu/record/885},
      doi = {https://doi.org/10.6083/M4833Q22},
}