no code implementations • 26 Apr 2024 • Anubhav Bhatti, Prithila Angkan, Behnam Behinaein, Zunayed Mahmud, Dirk Rodenburg, Heather Braund, P. James Mclellan, Aaron Ruberto, Geoffery Harrison, Daryl Wilson, Adam Szulewski, Dan Howes, Ali Etemad, Paul Hungler
In contrast, for LOSO, the best performance is achieved by the deep learning model with ECG, EDA, and EEG.
no code implementations • 1 Aug 2023 • Dustin Pulver, Prithila Angkan, Paul Hungler, Ali Etemad
We pre-train our model using self-supervised masked autoencoding on emotion-related EEG datasets and use transfer learning with both frozen weights and fine-tuning to perform downstream cognitive load classification.
1 code implementation • 9 Apr 2023 • Prithila Angkan, Behnam Behinaein, Zunayed Mahmud, Anubhav Bhatti, Dirk Rodenburg, Paul Hungler, Ali Etemad
Through this paper, we introduce a novel driver cognitive load assessment dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with other physiological signals such as Electrocardiography (ECG) and Electrodermal Activity (EDA) as well as eye tracking data.