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Found 6 papers, 2 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.
FACT: Learning Governing Abstractions Behind Integer Sequences
Integer sequences are of central importance to the modeling of concepts admitting complete finitary descriptions.
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.
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.
KDSalBox: A toolbox of efficient knowledge-distilled saliency models
Dozens of saliency models have been designed over the last few decades, targeted at diverse applications ranging from image compression and retargeting to robot navigation, surveillance, and distractor detection.
