Automatic drowsiness detection for safety-critical operations using ensemble models and EEG signals
PMS Ramos, CBS Maior, MC Moura, ID Lins - Process Safety and …, 2022 - Elsevier
Recently, industrial sectors that stage occupational and environment safety critical tasks,
such as the oil and gas industry, have been interested in monitoring biological parameters to …
such as the oil and gas industry, have been interested in monitoring biological parameters to …
[HTML][HTML] The sense of agency in human–AI interactions
Sense of agency (SoA) is the perceived control over one's actions and their consequences,
and through this one feels responsible for the consequent outcomes in the world. We …
and through this one feels responsible for the consequent outcomes in the world. We …
[HTML][HTML] Overview of methods and available tools used in complex brain disorders
Complex brain disorders, including Alzheimer's dementia, sleep disorders, and epilepsy, are
chronic conditions that have high prevalence individually and in combination, increasing …
chronic conditions that have high prevalence individually and in combination, increasing …
[HTML][HTML] EEGraph: An open-source Python library for modeling electroencephalograms using graphs
Background and objective Connectivity studies make it possible to identify alterations in
brain connections and to associate these pathologies with different neurological disorders …
brain connections and to associate these pathologies with different neurological disorders …
[HTML][HTML] PeyeMMV: Python implementation of EyeMMV's fixation detection algorithm
V Krassanakis - Software Impacts, 2023 - Elsevier
This article presents a new Python module called PeyeMMV. The module implements the
two-step spatial dispersion fixation detection algorithm imported in both EyeMMV and …
two-step spatial dispersion fixation detection algorithm imported in both EyeMMV and …
Quantum machine learning for drowsiness detection with EEG signals
Human reliability is an increasingly important area in various fields for accident prevention.
Monitoring human biological parameters, such as metabolic agents, through techniques like …
Monitoring human biological parameters, such as metabolic agents, through techniques like …
An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children
E van Twist, FW Hiemstra, ABG Cramer… - Journal of Clinical …, 2024 - jcsm.aasm.org
Study Objectives: Although sleep is frequently disrupted in the pediatric intensive care unit, it
is currently not possible to perform real-time sleep monitoring at the bedside. In this study …
is currently not possible to perform real-time sleep monitoring at the bedside. In this study …
Imagined speech classification exploiting EEG power spectrum features
Imagined speech recognition has developed as a significant topic of research in the field of
brain-computer interfaces. This innovative technique has great promise as a communication …
brain-computer interfaces. This innovative technique has great promise as a communication …
Predicting the therapeutic response to valproic acid in childhood absence epilepsy through electroencephalogram analysis using machine learning
SP Li, LC Lin, RC Yang, CS Ouyang, YH Chiu… - Epilepsy & Behavior, 2024 - Elsevier
Childhood absence epilepsy (CAE) is a common type of idiopathic generalized epilepsy,
manifesting as daily multiple absence seizures. Although seizures in most patients can be …
manifesting as daily multiple absence seizures. Although seizures in most patients can be …
Predicting cognitive load with EEG using Riemannian geometry-based features
Objective. We show that electroencephalography (EEG)-based cognitive load (CL)
prediction using Riemannian geometry features outperforms existing models. The …
prediction using Riemannian geometry features outperforms existing models. The …