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Found 10 papers, 5 papers with code
An Interpretable and Attention-based Method for Gaze Estimation Using Electroencephalography
Eye movements can reveal valuable insights into various aspects of human mental processes, physical well-being, and actions.
Electrode Clustering and Bandpass Analysis of EEG Data for Gaze Estimation
In this study, we validate the findings of previously published papers, showing the feasibility of an Electroencephalography (EEG) based gaze estimation.
Detection of ADHD based on Eye Movements during Natural Viewing
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that is highly prevalent and requires clinical specialists to diagnose.
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications
Here, we introduce DETRtime, a novel framework for time-series segmentation that creates ocular event detectors that do not require additionally recorded eye-tracking modality and rely solely on EEG data.
Reading Task Classification Using EEG and Eye-Tracking Data
The Zurich Cognitive Language Processing Corpus (ZuCo) provides eye-tracking and EEG signals from two reading paradigms, normal reading and task-specific reading.
EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction
We present a new dataset and benchmark with the goal of advancing research in the intersection of brain activities and eye movements.
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing
In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial.
ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation
We recorded and preprocessed ZuCo 2. 0, a new dataset of simultaneous eye-tracking and electroencephalography during natural reading and during annotation.
CogniVal: A Framework for Cognitive Word Embedding Evaluation
An interesting method of evaluating word representations is by how much they reflect the semantic representations in the human brain.
Advancing NLP with Cognitive Language Processing Signals
Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks.
