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.
1 code implementation • 18 Jun 2022 • Zunayed Mahmud, Paul Hungler, Ali Etemad
The eye region isolation is performed with a U-Net style network which we train using a synthetic dataset that contains eye region masks for the visible eyeball and the iris region.
no code implementations • 9 Jun 2022 • Anubhav Bhatti, Behnam Behinaein, Paul Hungler, Ali Etemad
We perform extensive experiments on three public multimodal wearable datasets, WESAD, SWELL-KW, and CASE, and demonstrate that our method can effectively regulate and share information between different modalities to learn better representations.
no code implementations • 15 Dec 2021 • Zunayed Mahmud, Paul Hungler, Ali Etemad
We first create a synthetic dataset containing eye region masks detailing the visible eyeball and iris using a simulator.
no code implementations • 22 Aug 2021 • Behnam Behinaein, Anubhav Bhatti, Dirk Rodenburg, Paul Hungler, Ali Etemad
Electrocardiogram (ECG) has been widely used for emotion recognition.
no code implementations • 4 Aug 2021 • Anubhav Bhatti, Behnam Behinaein, Dirk Rodenburg, Paul Hungler, Ali Etemad
Classification of human emotions can play an essential role in the design and improvement of human-machine systems.
no code implementations • 24 Aug 2020 • Kyle Ross, Paul Hungler, Ali Etemad
The results show the wide-spread applicability for stacked convolutional autoencoders to be used with machine learning for affective computing.
no code implementations • 31 Jul 2019 • Pritam Sarkar, Kyle Ross, Aaron J. Ruberto, Dirk Rodenburg, Paul Hungler, Ali Etemad
Simulations are a pedagogical means of enabling a risk-free way for healthcare practitioners to learn, maintain, or enhance their knowledge and skills.