AIMC Topic: Electroencephalography
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Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning.
Ergonomics
May 7, 2020
This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work composed of mental arithmetic tasks with different levels of difficulty was designed and used in the experi...
Analyzing the Effectiveness of the Brain-Computer Interface for Task Discerning Based on Machine Learning.
Sensors (Basel, Switzerland)
Apr 23, 2020
The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using ...
Neural memory plasticity for medical anomaly detection.
Neural networks : the official journal of the International Neural Network Society
Apr 18, 2020
In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling. However,...
A probabilistic approach for calibration time reduction in hybrid EEG-fTCD brain-computer interfaces.
Biomedical engineering online
Apr 16, 2020
BACKGROUND: Generally, brain-computer interfaces (BCIs) require calibration before usage to ensure efficient performance. Therefore, each BCI user has to attend a certain number of calibration sessions to be able to use the system. However, such cali...
A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data.
International journal of environmental research and public health
Apr 9, 2020
This paper focuses on quantifying the uncertainty in the specific absorption rate valuesof the brain induced by the uncertain positions of the electroencephalography electrodes placed onthe patient's scalp. To avoid running a large number of simulati...
A Novel Deep Neural Network for Robust Detection of Seizures Using EEG Signals.
Computational and mathematical methods in medicine
Apr 7, 2020
The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures. Manual recognition is a time-consuming and laborious process that places a heavy burden on neurologists, and he...
Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls.
Psychiatry research
Apr 6, 2020
Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning techniques, we explored whether abnormalities in microstates can be used to classify patients with s...
Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.
Sensors (Basel, Switzerland)
Apr 4, 2020
Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate severa...
Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Apr 2, 2020
OBJECTIVE: Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals.
An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Apr 2, 2020
OBJECTIVE: To validate an artificial intelligence-based computer algorithm for detection of epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy.
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