AIMC Topic: Electroencephalography
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Dyslexia Diagnosis by EEG Temporal and Spectral Descriptors: An Anomaly Detection Approach.
International journal of neural systems
Jun 4, 2020
Diagnosis of learning difficulties is a challenging goal. There are huge number of factors involved in the evaluation procedure that present high variance among the population with the same difficulty. Diagnosis is usually performed by scoring subjec...
Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach.
Clinical EEG and neuroscience
Jun 3, 2020
The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effect...
Single-Trial EEG Responses Classified Using Latency Features.
International journal of neural systems
Jun 2, 2020
Covert attention has been repeatedly shown to impact on EEG responses after single and repeated practice sessions. Machine learning techniques are increasingly adopted to classify single-trial EEG responses thereby primarily relying on amplitude-base...
Emotion recognition with convolutional neural network and EEG-based EFDMs.
Neuropsychologia
Jun 1, 2020
Electroencephalogram (EEG), as a direct response to brain activity, can be used to detect mental states and physical conditions. Among various EEG-based emotion recognition studies, due to the non-linear, non-stationary and the individual difference ...
Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial.
JAMA network open
Jun 1, 2020
IMPORTANCE: Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted.
Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination.
Neural networks : the official journal of the International Neural Network Society
May 29, 2020
Convolutional neural networks (CNNs) are emerging as powerful tools for EEG decoding: these techniques, by automatically learning relevant features for class discrimination, improve EEG decoding performances without relying on handcrafted features. N...
Classifying creativity: Applying machine learning techniques to divergent thinking EEG data.
NeuroImage
May 28, 2020
Prior research has shown that greater EEG alpha power (8-13 Hz) is characteristic of more creative individuals, and more creative task conditions. The present study investigated the potential for machine learning to classify more and less creative b...
A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End.
IEEE transactions on biomedical circuits and systems
May 28, 2020
In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learning framework, AnesNET. An EEG analog front-end (AFE) that can compensate ±380-mV electrode DC offset...
EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia.
International journal of neural systems
May 28, 2020
The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography...
Pilot study: can machine learning analyses of movement discriminate between leg movements in sleep (LMS) with vs. without cortical arousals?
Sleep & breathing = Schlaf & Atmung
May 26, 2020
PURPOSE: Clinical and animal studies indicate frequent small micro-arousals (McA) fragment sleep leading to health complications. McA in humans is defined by changes in EEG and EMG during sleep. Complex EEG recordings during the night are usually req...
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