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Electrooculography

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An exploratory analysis of longitudinal artificial intelligence for cognitive fatigue detection using neurophysiological based biosignal data.

Scientific reports
Cognitive fatigue is a psychological condition characterized by opinions of fatigue and weakened cognitive functioning owing to constant stress. Cognitive fatigue is a critical condition that can significantly impair attention and performance, among ...

AFSleepNet: Attention-Based Multi-View Feature Fusion Framework for Pediatric Sleep Staging.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The widespread prevalence of sleep problems in children highlights the importance of timely and accurate sleep staging in the diagnosis and treatment of pediatric sleep disorders. However, most existing sleep staging methods rely on one-dimensional r...

Comparison of automated deep neural network against manual sleep stage scoring in clinical data.

Computers in biology and medicine
OBJECTIVE: To compare the accuracy and generalizability of an automated deep neural network and the Philip Sleepware G3™ Somnolyzer system (Somnolyzer) for sleep stage scoring using American Academy of Sleep Medicine (AASM) guidelines.

SleepGCN: A transition rule learning model based on Graph Convolutional Network for sleep staging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic sleep staging is essential for assessing and diagnosing sleep disorders, serving millions of people who suffer from them. Numerous sleep staging models have been proposed recently, but most of them have not fully e...

Residual Self-Calibrated Network With Multi-Scale Channel Attention for Accurate EOG-Based Eye Movement Classification.

IEEE journal of biomedical and health informatics
Recently, Electrooculography-based Human-Computer Interaction (EOG-HCI) technology has gained widespread attention in industrial areas, including assistive robots, augmented reality in gaming, etc. However, as the fundamental step of EOG-HCI, accurat...

A Multimodal Consistency-Based Self-Supervised Contrastive Learning Framework for Automated Sleep Staging in Patients With Disorders of Consciousness.

IEEE journal of biomedical and health informatics
Sleep is a fundamental human activity, and automated sleep staging holds considerable investigational potential. Despite numerous deep learning methods proposed for sleep staging that exhibit notable performance, several challenges remain unresolved,...

Mortality risk assessment using deep learning-based frequency analysis of electroencephalography and electrooculography in sleep.

Sleep
STUDY OBJECTIVES: To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality.

Eye movement detection using electrooculography and machine learning in cardiac arrest patients.

Resuscitation
AIM: To train a machine learning algorithm to identify eye movement from electrooculography (EOG) in cardiac arrest (CA) patients. Neuroprognostication of comatose post-CA patients is challenging, requiring novel biomarkers to guide decision making. ...

Multimodal machine learning for deception detection using behavioral and physiological data.

Scientific reports
Deception detection is crucial in domains like national security, privacy, judiciary, and courtroom trials. Differentiating truth from lies is inherently challenging due to many complex, diversified behavioural, physiological and cognitive aspects. T...

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients.

Journal of visualized experiments : JoVE
This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes electroencephalogram (EEG) and electrooculogram (EOG) signals to help users assist upper limb function in...