AIMC Topic: Electroencephalography

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Benefits of deep learning classification of continuous noninvasive brain-computer interface control.

Journal of neural engineering Jun 9, 2021
. Noninvasive brain-computer interfaces (BCIs) assist paralyzed patients by providing access to the world without requiring surgical intervention. Prior work has suggested that EEG motor imagery based BCI can benefit from increased decoding accuracy ...
Humans Electroencephalography Brain-Computer Interfaces Imagination Neural Networks, Computer Algorithms Deep Learning
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Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals.

Scientific reports Jun 8, 2021
This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning a...
Neural Networks, Computer Algorithms Brain Humans Adult Male Child Computer Simulation Learning Female Neurons Brain-Computer Interfaces Nerve Net Models, Neurological Electroencephalography Signal Processing, Computer-Assisted Imaging, Three-Dimensional Magnetic Resonance Imaging
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Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG.

Brain and language Jun 7, 2021
Currently there are no reliable means of identifying infants at-risk for later language disorders. Infant neural responses to rhythmic stimuli may offer a solution, as neural tracking of rhythm is atypical in children with developmental language diso...
Electroencephalography Machine Learning Infant Speech Child Support Vector Machine Humans Neural Networks, Computer
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Depression Diagnosis Modeling With Advanced Computational Methods: Frequency-Domain eMVAR and Deep Learning.

Clinical EEG and neuroscience Jun 3, 2021
Electroencephalogram (EEG)-based automated depression diagnosis systems have been suggested for early and accurate detection of mood disorders. EEG signals are highly irregular, nonlinear, and nonstationary in nature and are traditionally studied fro...
Electroencephalography Signal Processing, Computer-Assisted Deep Learning Depression Humans Algorithms Brain
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Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning.

Physical and engineering sciences in medicine May 27, 2021
Early diagnosis of attention deficit and hyperactivity disorder (ADHD) by experts is difficult. Some solutions using electroencephalography (EEG) signals have been presented in the literature to solve this problem. However, few studies have aimed to ...
Humans Neural Networks, Computer Deep Learning Support Vector Machine Electroencephalography Attention Deficit Disorder with Hyperactivity
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Epileptic Seizures Detection Using Deep Learning Techniques: A Review.

International journal of environmental research and public health May 27, 2021
A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches i...
Artificial Intelligence Algorithms Deep Learning Humans Electroencephalography Epilepsy Seizures
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Real-time, automatic, open-source sleep stage classification system using single EEG for mice.

Scientific reports May 27, 2021
We developed a real-time sleep stage classification system with a convolutional neural network using only a one-channel electro-encephalogram source from mice and universally available features in any time-series data: raw signal, spectrum, and zeitg...
Sleep Stages Superficial Back Muscles Signal Processing, Computer-Assisted Electromyography Electrodes Neural Networks, Computer Mice Algorithms Electroencephalography Animals
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Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning.

International journal of neural systems May 26, 2021
Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using ...
Electroencephalography Epilepsy Seizures Humans Neural Networks, Computer Deep Learning
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Expression-EEG Bimodal Fusion Emotion Recognition Method Based on Deep Learning.

Computational and mathematical methods in medicine May 25, 2021
As one of the key issues in the field of emotional computing, emotion recognition has rich application scenarios and important research value. However, the single biometric recognition in the actual scene has the problem of low accuracy of emotion re...
Facial Expression Deep Learning Algorithms Emotions Computational Biology Humans Databases, Factual Electroencephalography Neural Networks, Computer Computer Simulation Pattern Recognition, Automated
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Delayed brain development of Rolandic epilepsy profiled by deep learning-based neuroanatomic imaging.

European radiology May 20, 2021
OBJECTIVES: Although Rolandic epilepsy (RE) has been regarded as a brain developmental disorder, neuroimaging studies have not yet ascertained whether RE has brain developmental delay. This study employed deep learning-based neuroanatomic biomarker t...
Epilepsy, Rolandic Magnetic Resonance Imaging Electroencephalography Humans Brain Deep Learning
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