AIMC Topic: Electroencephalography
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A CNN identified by reinforcement learning-based optimization framework for EEG-based state evaluation.
Journal of neural engineering
May 18, 2021
Electroencephalogram (EEG) data, as a kind of complex time-series, is one of the most widely-used information measurements for evaluating human psychophysiological states. Recently, numerous works applied deep learning techniques, especially the conv...
EEG microstate features for schizophrenia classification.
PloS one
May 14, 2021
Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes ...
Deep learning applied to electroencephalogram data in mental disorders: A systematic review.
Biological psychology
May 13, 2021
In recent medical research, tremendous progress has been made in the application of deep learning (DL) techniques. This article systematically reviews how DL techniques have been applied to electroencephalogram (EEG) data for diagnostic and predictiv...
Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier.
Neuroscience research
May 11, 2021
Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in sleep research and must be carried out with a high degree of accuracy bec...
A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.
Sensors (Basel, Switzerland)
May 11, 2021
Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requiremen...
Expression EEG Multimodal Emotion Recognition Method Based on the Bidirectional LSTM and Attention Mechanism.
Computational and mathematical methods in medicine
May 11, 2021
Due to the complexity of human emotions, there are some similarities between different emotion features. The existing emotion recognition method has the problems of difficulty of character extraction and low accuracy, so the bidirectional LSTM and at...
Activation patterns of interictal epileptiform discharges in relation to sleep and seizures: An artificial intelligence driven data analysis.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
May 8, 2021
OBJECTIVE: To quantify effects of sleep and seizures on the rate of interictal epileptiform discharges (IED) and to classify patients with epilepsy based on IED activation patterns.
Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals.
Computers in biology and medicine
May 6, 2021
Emotion is interpreted as a psycho-physiological process, and it is associated with personality, behavior, motivation, and character of a person. The objective of affective computing is to recognize different types of emotions for human-computer inte...
Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia.
PloS one
May 6, 2021
In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identi...
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 5, 2021
Automatic sleep stage mymargin classification is of great importance to measure sleep quality. In this paper, we propose a novel attention-based deep learning architecture called AttnSleep to classify sleep stages using single channel EEG signals. Th...
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