59 citations · 178 across the 13 of their papers we have counts for
13 papers
Understanding Mental States in Active and Autonomous Driving with EEG
Prithila Angkan, Paul Hungler, Ali Etemad
Understanding how driver mental states differ between active and autonomous driving is critical for designing safe human-vehicle interfaces. This paper presents the first EEG-based…
Graph-Based Learning of Spectro-Topographical EEG Representations with Gradient Alignment for Brain-Computer Interfaces
Prithila Angkan, Amin Jalali, Paul Hungler +1
We present a novel graph-based learning of EEG representations with gradient alignment (GEEGA) that leverages multi-domain information to learn EEG representations for brain-comput…
Multi-Domain EEG Representation Learning with Orthogonal Mapping and Attention-based Fusion for Cognitive Load Classification
Prithila Angkan, Amin Jalali, Paul Hungler +1
We propose a new representation learning solution for the classification of cognitive load based on Electroencephalogram (EEG). Our method integrates both time and frequency domain…
CLARE: Cognitive Load Assessment in REaltime with Multimodal Data
Anubhav Bhatti, Prithila Angkan, Behnam Behinaein +11
We present a novel multimodal dataset for Cognitive Load Assessment in REal-time (CLARE). The dataset contains physiological and gaze data from 24 participants with self-reported c…
EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning
Dustin Pulver, Prithila Angkan, Paul Hungler +1
Cognitive load, the amount of mental effort required for task completion, plays an important role in performance and decision-making outcomes, making its classification and analysi…
Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines
Prithila Angkan, Behnam Behinaein, Zunayed Mahmud +4
Through this paper, we introduce a novel driver cognitive load assessment dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with other physiological signal…
AttX: Attentive Cross-Connections for Fusion of Wearable Signals in Emotion Recognition
Anubhav Bhatti, Behnam Behinaein, Paul Hungler +1
We propose cross-modal attentive connections, a new dynamic and effective technique for multimodal representation learning from wearable data. Our solution can be integrated into a…
Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning
Zunayed Mahmud, Paul Hungler, Ali Etemad
We propose a novel neural pipeline, MSGazeNet, that learns gaze representations by taking advantage of the eye anatomy information through a multistream framework. Our proposed sol…
Gaze Estimation with Eye Region Segmentation and Self-Supervised Multistream Learning
Zunayed Mahmud, Paul Hungler, Ali Etemad
We present a novel multistream network that learns robust eye representations for gaze estimation. We first create a synthetic dataset containing eye region masks detailing the vis…
A Transformer Architecture for Stress Detection from ECG
Behnam Behinaein, Anubhav Bhatti, Dirk Rodenburg +2
Electrocardiogram (ECG) has been widely used for emotion recognition. This paper presents a deep neural network based on convolutional layers and a transformer mechanism to detect…
Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition
Anubhav Bhatti, Behnam Behinaein, Dirk Rodenburg +2
Classification of human emotions can play an essential role in the design and improvement of human-machine systems. While individual biological signals such as Electrocardiogram (E…
Unsupervised Multi-Modal Representation Learning for Affective Computing with Multi-Corpus Wearable Data
Kyle Ross, Paul Hungler, Ali Etemad
With recent developments in smart technologies, there has been a growing focus on the use of artificial intelligence and machine learning for affective computing to further enhance…
Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning
Pritam Sarkar, Kyle Ross, Aaron J. Ruberto +3
Simulations are a pedagogical means of enabling a risk-free way for healthcare practitioners to learn, maintain, or enhance their knowledge and skills. Such simulations should prov…