AIMC Topic: Electroencephalography

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Seizure Types Classification by Generating Input Images With in-Depth Features From Decomposed EEG Signals for Deep Learning Pipeline.

IEEE journal of biomedical and health informatics Oct 4, 2022
Electroencephalogram (EEG) based seizure types classification has not been addressed well, compared to seizure detection, which is very important for the diagnosis and prognosis of epileptic patients. The minuscule changes reflected in EEG signals am...
Seizures Wavelet Analysis Humans Deep Learning Electroencephalography Epilepsy
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An Improved Neural Network Based on SENet for Sleep Stage Classification.

IEEE journal of biomedical and health informatics Oct 4, 2022
Sleep staging is an important step in analyzing sleep quality. Traditional manual analysis by psychologists is time-consuming. In this paper, we propose an automatic sleep staging model with an improved attention module and hidden Markov model (HMM)....
Humans Neural Networks, Computer Polysomnography Sleep Stages Electroencephalography Sleep
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Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning.

IEEE transactions on medical imaging Sep 30, 2022
Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, t...
Sensitivity and Specificity Deep Learning Humans Epilepsy Image Processing, Computer-Assisted Magnetoencephalography Electroencephalography
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Improved Manual Annotation of EEG Signals through Convolutional Neural Network Guidance.

eNeuro Sep 29, 2022
The development of validated algorithms for automated handling of artifacts is essential for reliable and fast processing of EEG signals. Recently, there have been methodological advances in designing machine-learning algorithms to improve artifact d...
Artifacts Child Electroencephalography Machine Learning Algorithms Humans Neural Networks, Computer
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Three simple steps to improve the interpretability of EEG-SVM studies.

Journal of neurophysiology Sep 28, 2022
Machine-learning systems that classify electroencephalography (EEG) data offer important perspectives for the diagnosis and prognosis of a wide variety of neurological and psychiatric conditions, but their clinical adoption remains low. We propose he...
Support Vector Machine Electroencephalography Machine Learning Algorithms
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Localizing seizure onset zones in surgical epilepsy with neurostimulation deep learning.

Journal of neurosurgery Sep 23, 2022
OBJECTIVE: In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization that use brief interictal recordings could supplement presurgical evaluations and improve care. Thus, the authors sought to localize SOZs b...
Drug Resistant Epilepsy Humans Deep Learning Seizures Electroencephalography Epilepsy, Temporal Lobe Epilepsy
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Investigating Methods for Cognitive Workload Estimation for Assistive Robots.

Sensors (Basel, Switzerland) Sep 9, 2022
Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing mod...
Bayes Theorem Electroencephalography Workload Cognition Robotics Humans
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A novel approach for detection of dyslexia using convolutional neural network with EOG signals.

Medical & biological engineering & computing Sep 5, 2022
Dyslexia is a learning disability in acquiring reading skills, even though the individual has the appropriate learning opportunity, adequate education, and appropriate sociocultural environment. Dyslexia negatively affects children's educational deve...
Electroencephalography Electrooculography User-Computer Interface Child Dyslexia Humans Neural Networks, Computer Eye Movements
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Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN Autoencoder.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 2, 2022
This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated with noise during the recording process, mostly due to muscle artifacts ...
Artifacts Movement Algorithms Humans Electroencephalography Neural Networks, Computer
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An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works.

Computers in biology and medicine Sep 1, 2022
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood...
Humans Seizures Neuroimaging Electroencephalography Algorithms Epilepsy Deep Learning
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