AIMC Topic: Electroencephalography

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Stacked autoencoders as new models for an accurate Alzheimer's disease classification support using resting-state EEG and MRI measurements.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Oct 15, 2020
OBJECTIVE: This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsE...
Alzheimer Disease Magnetic Resonance Imaging Electroencephalography Humans Brain Retrospective Studies Neural Networks, Computer
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A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG.

Journal of neural engineering Oct 15, 2020
OBJECTIVE: When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature o...
Humans Neural Networks, Computer Electroencephalography Artifacts Amplifiers, Electronic Algorithms Deep Learning
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Data augmentation for enhancing EEG-based emotion recognition with deep generative models.

Journal of neural engineering Oct 14, 2020
OBJECTIVE: The data scarcity problem in emotion recognition from electroencephalography (EEG) leads to difficulty in building an affective model with high accuracy using machine learning algorithms or deep neural networks. Inspired by emerging deep g...
Algorithms Neural Networks, Computer Emotions Machine Learning Electroencephalography
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Thinker invariance: enabling deep neural networks for BCI across more people.

Journal of neural engineering Oct 13, 2020
OBJECTIVE: Most deep neural networks (DNNs) used as brain computer interfaces (BCI) classifiers are rarely viable for more than one person and are relatively shallow compared to the state-of-the-art in the wider machine learning literature. The goal ...
Machine Learning Electroencephalography Brain-Computer Interfaces Humans Neural Networks, Computer
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DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms.

Scientific reports Oct 2, 2020
Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging...
Electrocardiography Electroencephalography Patient Care Deep Learning Models, Theoretical Humans Algorithms Software Machine Learning Electronic Health Records
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Improvement of P300-Based Brain-Computer Interfaces for Home Appliances Control by Data Balancing Techniques.

Sensors (Basel, Switzerland) Sep 29, 2020
The oddball paradigm used in P300-based brain-computer interfaces (BCIs) intrinsically poses the issue of data imbalance between target stimuli and nontarget stimuli. Data imbalance can cause overfitting problems and, consequently, poor classificatio...
Support Vector Machine Electroencephalography Brain-Computer Interfaces Humans
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EEG-Based Epilepsy Recognition via Multiple Kernel Learning.

Computational and mathematical methods in medicine Sep 29, 2020
In the field of brain-computer interfaces, it is very common to use EEG signals for disease diagnosis. In this study, a style regularized least squares support vector machine based on multikernel learning is proposed and applied to the recognition of...
Mathematical Concepts Algorithms Humans Support Vector Machine Epilepsy Diagnosis, Computer-Assisted Electroencephalography Machine Learning Least-Squares Analysis Computational Biology Signal Processing, Computer-Assisted
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Electroencephalography Might Improve Diagnosis of Acute Stroke and Large Vessel Occlusion.

Stroke Sep 18, 2020
BACKGROUND AND PURPOSE: Clinical methods have incomplete diagnostic value for early diagnosis of acute stroke and large vessel occlusion (LVO). Electroencephalography is rapidly sensitive to brain ischemia. This study examined the diagnostic utility ...
Hemorrhagic Stroke Electroencephalography Stroke Ischemic Attack, Transient Aged, 80 and over Sensitivity and Specificity Logistic Models Middle Aged Ischemic Stroke Deep Learning Male Neural Networks, Computer Female Humans Aged
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FusionSense: Emotion Classification Using Feature Fusion of Multimodal Data and Deep Learning in a Brain-Inspired Spiking Neural Network.

Sensors (Basel, Switzerland) Sep 17, 2020
Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocar...
Humans Neural Networks, Computer Deep Learning Emotions Brain Electroencephalography
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EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities.

Computational intelligence and neuroscience Sep 16, 2020
Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing ...
Classification Machine Learning Adolescent Female Adult Emotions Humans Young Adult Male Electroencephalography
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