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 …

[HTML][HTML] The sense of agency in human–AI interactions

R Legaspi, W Xu, T Konishi, S Wada… - Knowledge-Based …, 2024 - Elsevier
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 …

[HTML][HTML] Overview of methods and available tools used in complex brain disorders

L Ilias, G Doukas, M Kontoulis, K Alexakis… - Open Research …, 2023 - ncbi.nlm.nih.gov
Complex brain disorders, including Alzheimer's dementia, sleep disorders, and epilepsy, are
chronic conditions that have high prevalence individually and in combination, increasing …

[HTML][HTML] EEGraph: An open-source Python library for modeling electroencephalograms using graphs

AM Maitin, A Nogales, P Chazarra, ÁJ García-Tejedor - Neurocomputing, 2023 - Elsevier
Background and objective Connectivity studies make it possible to identify alterations in
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 …

Quantum machine learning for drowsiness detection with EEG signals

ID Lins, LMM Araújo, CBS Maior… - Process Safety and …, 2024 - Elsevier
Human reliability is an increasingly important area in various fields for accident prevention.
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 …

Imagined speech classification exploiting EEG power spectrum features

A Hossain, P Khan, MF Kader - Medical & Biological Engineering & …, 2024 - Springer
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 …

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 …

Predicting cognitive load with EEG using Riemannian geometry-based features

I Kremer, W Halimi, A Walshe, M Cerf… - Journal of Neural …, 2024 - iopscience.iop.org
Objective. We show that electroencephalography (EEG)-based cognitive load (CL)
prediction using Riemannian geometry features outperforms existing models. The …