AIMC Topic: Electroencephalography

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Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Nov 15, 2018
OBJECTIVE: Automatic detection of epileptic seizures based on deep learning methods received much attention last year. However, the potential of deep neural networks in seizure detection has not been fully exploited in terms of the optimal design of ...
Neural Networks, Computer Deep Learning Humans Electroencephalography Seizures Signal Processing, Computer-Assisted
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Soft Computing for Analysis of Biomedical Data.

Computational and mathematical methods in medicine Nov 15, 2018
Electroencephalography Computational Biology Humans Data Mining Magnetic Resonance Imaging Machine Learning Data Interpretation, Statistical Gene Regulatory Networks
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Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy.

Journal of sleep research Nov 13, 2018
Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here...
Adult Male Electroencephalography Sleep Stages Humans Actigraphy Machine Learning Sleep Wake Disorders Female
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Frameless robot-assisted stereoelectroencephalography for refractory epilepsy in pediatric patients: accuracy, usefulness, and technical issues.

Acta neurochirurgica Nov 9, 2018
BACKGROUND: Stereoelectroencephalography (SEEG) is an effective technique to help to locate and to delimit the epileptogenic area and/or to define relationships with functional cortical areas. We intend to describe the surgical technique and verify t...
Child Robotics Electroencephalography Electrodes, Implanted Stereotaxic Techniques Drug Resistant Epilepsy Clinical Decision-Making Female Male Postoperative Complications Adolescent Child, Preschool Humans
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Coarse-to-fine information integration in human vision.

NeuroImage Nov 4, 2018
Coarse-to-fine theories of vision propose that the coarse information carried by the low spatial frequencies (LSF) of visual input guides the integration of finer, high spatial frequency (HSF) detail. Whether and how LSF modulates HSF processing in n...
Male Time Factors Support Vector Machine Female Young Adult Adult Evoked Potentials Visual Perception Facial Recognition Electroencephalography Pattern Recognition, Automated Cerebral Cortex Humans
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Unity and diversity in working memory load: Evidence for the separability of the executive functions updating and inhibition using machine learning.

Biological psychology Nov 4, 2018
OBJECTIVE: According to current theoretical models of working memory (WM), executive functions (EFs) like updating, inhibition and shifting play an important role in WM functioning. The models state that EFs highly correlate with each other but also ...
Humans Memory, Short-Term Psychomotor Performance Male Cerebral Cortex Support Vector Machine Female Inhibition, Psychological Electroencephalography Young Adult Executive Function Adult
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Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses.

Scientific reports Nov 2, 2018
Neurophysiological features like event-related potentials (ERPs) have long been used to identify different cognitive sub-processes that may contribute to task performance. It has however remained unclear whether "classical" ERPs are truly the best re...
Male Models, Neurological Electroencephalography Reaction Time Female Young Adult Humans Adult Machine Learning Neurophysiology Motor Cortex Healthy Volunteers Behavior Observation Techniques Evoked Potentials Biological Variation, Population Inhibition, Psychological
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A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory.

Behavioral and brain functions : BBF Oct 31, 2018
BACKGROUND: Emotion recognition is an increasingly important field of research in brain computer interactions.
Brain Female Young Adult Electroencephalography Humans Adult Music Machine Learning Video Recording Recognition, Psychology Male Databases, Factual Emotions
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EEG may serve as a biomarker in Huntington's disease using machine learning automatic classification.

Scientific reports Oct 31, 2018
Reliable markers measuring disease progression in Huntington's disease (HD), before and after disease manifestation, may guide a therapy aimed at slowing or halting disease progression. Quantitative electroencephalography (qEEG) may provide a quantif...
Case-Control Studies ROC Curve Electroencephalography Huntington Disease Female Male Middle Aged Humans Machine Learning Biomarkers
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Identification of Hidden Sources by Estimating Instantaneous Causality in High-Dimensional Biomedical Time Series.

International journal of neural systems Oct 29, 2018
The study of connectivity patterns of a system's variables, such as multi-channel electroencephalograms (EEG), is of utmost importance towards a better understanding of its internal evolutionary mechanisms. Here, the problem of estimating the connect...
Humans Neural Networks, Computer Computer Simulation Electroencephalography Epilepsy
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