Graph-based deep learning for medical diagnosis and analysis: past, present and future
With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …
problems have been tackled. It has become critical to explore how machine learning and …
Cognitive workload recognition using EEG signals and machine learning: A review
Machine learning and its subfield deep learning techniques provide opportunities for the
development of operator mental state monitoring, especially for cognitive workload …
development of operator mental state monitoring, especially for cognitive workload …
Multi-modal bioelectrical signal fusion analysis based on different acquisition devices and scene settings: Overview, challenges, and novel orientation
J Li, Q Wang - Information Fusion, 2022 - Elsevier
Multi-modal fusion combines multiple modal information to overcome the limitation of
incomplete information expressed by a single modality, so as to realize the complementarity …
incomplete information expressed by a single modality, so as to realize the complementarity …
FGANet: fNIRS-guided attention network for hybrid EEG-fNIRS brain-computer interfaces
Non-invasive brain-computer interfaces (BCIs) have been widely used for neural decoding,
linking neural signals to control devices. Hybrid BCI systems using electroencephalography …
linking neural signals to control devices. Hybrid BCI systems using electroencephalography …
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
A Shoeibi, P Moridian, M Khodatars… - Computers in biology …, 2022 - Elsevier
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
Subject-specific cognitive workload classification using EEG-based functional connectivity and deep learning
Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other
real-time and high-risk situations. Neuroimaging techniques have long been used for …
real-time and high-risk situations. Neuroimaging techniques have long been used for …
Recognition of the mental workloads of pilots in the cockpit using EEG signals
The commercial flightdeck is a naturally multi-tasking work environment, one in which
interruptions are frequent come in various forms, contributing in many cases to aviation …
interruptions are frequent come in various forms, contributing in many cases to aviation …
Cross-subject cognitive workload recognition based on eeg and deep domain adaptation
Regarding cognitive workload recognition (CWR), electroencephalography (EEG) signals
are nonstationary across time and vary from different subjects, thus hindering the cross …
are nonstationary across time and vary from different subjects, thus hindering the cross …
Cognitive workload estimation using physiological measures: a review
D Das Chakladar, PP Roy - Cognitive Neurodynamics, 2024 - Springer
Estimating cognitive workload levels is an emerging research topic in the cognitive
neuroscience domain, as participants' performance is highly influenced by cognitive …
neuroscience domain, as participants' performance is highly influenced by cognitive …
Exploring convolutional neural network architectures for EEG feature extraction
The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …
neural network (CNN) for extracting features from EEG signals. Our task was to understand …