AIMC Topic: Eye-Tracking Technology
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Integrating Eye Tracking With Grouped Fusion Networks for Semantic Segmentation on Mammogram Images.
IEEE transactions on medical imaging
Feb 4, 2025
Medical image segmentation has seen great progress in recent years, largely due to the development of deep neural networks. However, unlike in computer vision, high-quality clinical data is relatively scarce, and the annotation process is often a bur...
Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.
JMIR formative research
Jan 22, 2025
BACKGROUND: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions ...
Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples.
Translational psychiatry
Dec 31, 2024
Given the heterogeneous nature of attention-deficit/hyperactivity disorder (ADHD) and the absence of established biomarkers, accurate diagnosis and effective treatment remain a challenge in clinical practice. This study investigates the predictive ut...
Enhancing Autism Detection Through Gaze Analysis Using Eye Tracking Sensors and Data Attribution with Distillation in Deep Neural Networks.
Sensors (Basel, Switzerland)
Dec 5, 2024
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by differences in social communication and repetitive behaviors, often associated with atypical visual attention patterns. In this paper, the Gaze-Based Autism Classifier ...
Asymmetric Multi-Task Learning for Interpretable Gaze-Driven Grasping Action Forecasting.
IEEE journal of biomedical and health informatics
Dec 5, 2024
This work tackles the automatic prediction of grasping intention of humans observing their environment. Our target application is the assistance to people with motor disabilities and potential cognitive impairments, using assistive robotics. Our prop...
On the role of visual feedback and physiotherapist-patient interaction in robot-assisted gait training: an eye-tracking and HD-EEG study.
Journal of neuroengineering and rehabilitation
Dec 3, 2024
BACKGROUND: Treadmill based Robotic-Assisted Gait Training (t-RAGT) provides for automated locomotor training to help the patient achieve a physiological gait pattern, reducing the physical effort required by therapist. By introducing the robot as a ...
The use of machine learning to understand the role of visual attention in multi-attribute choice.
Acta psychologica
Nov 22, 2024
Whether eye movements (as a measure of visual attention) contribute to the understanding of how multi-attribute decisions are made, is still a matter of debate. In this study, we show how machine learning methods can be used to separate the effects o...
Decoding Continuous Tracking Eye Movements from Cortical Spiking Activity.
International journal of neural systems
Nov 15, 2024
Eye movements are the primary way primates interact with the world. Understanding how the brain controls the eyes is therefore crucial for improving human health and designing visual rehabilitation devices. However, brain activity is challenging to d...
Classification of Internal and External Distractions in an Educational VR Environment Using Multimodal Features.
IEEE transactions on visualization and computer graphics
Oct 11, 2024
Virtual reality (VR) can potentially enhance student engagement and memory retention in the classroom. However, distraction among participants in a VR-based classroom is a significant concern. Several factors, including mind wandering, external noise...
Integrating Large Language Model, EEG, and Eye-Tracking for Word-Level Neural State Classification in Reading Comprehension.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 20, 2024
With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning. This shift calls for inter...
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