AIMC Topic: Electroencephalography

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Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals.

Sensors (Basel, Switzerland) Jan 8, 2019
Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject's motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g....
Hand Brain-Computer Interfaces Movement Algorithms Electroencephalography Humans Neural Networks, Computer Machine Learning
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An Intelligent Sleep Apnea Classification System Based on EEG Signals.

Journal of medical systems Jan 8, 2019
Sleep Apnea is a sleep disorder which causes stop in breathing for a short duration of time that happens to human beings and animals during sleep. Electroencephalogram (EEG) plays a vital role in detecting the sleep apnea by sensing and recording the...
Adult Male Middle Aged Humans Neural Networks, Computer Machine Learning Signal Processing, Computer-Assisted Support Vector Machine Sleep Apnea Syndromes Polysomnography Electroencephalography
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Accuracy of robot-assisted versus optical frameless navigated stereoelectroencephalography electrode placement in children.

Journal of neurosurgery. Pediatrics Jan 4, 2019
OBJECTIVE The aim of this study was to compare the accuracy of optical frameless neuronavigation (ON) and robot-assisted (RA) stereoelectroencephalography (SEEG) electrode placement in children, and to identify factors that might increase the risk of...
Robotics Electroencephalography Brain Waves Child Stereotaxic Techniques Female Humans Optical Devices Imaging, Three-Dimensional Drug Resistant Epilepsy Brain Male Neuronavigation Electrodes, Implanted Magnetic Resonance Imaging Child, Preschool Retrospective Studies Tomography Scanners, X-Ray Computed
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Deep Convolutional Neural Networks for Feature-Less Automatic Classification of Independent Components in Multi-Channel Electrophysiological Brain Recordings.

IEEE transactions on bio-medical engineering Dec 24, 2018
OBJECTIVE: Interpretation of the electroencephalographic (EEG) and magnetoencephalographic (MEG) signals requires off-line artifacts removal. Since artifacts share frequencies with brain activity, filtering is insufficient. Blind source separation, m...
Algorithms Magnetoencephalography Humans Electroencephalography Brain Signal Processing, Computer-Assisted Deep Learning Artifacts
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Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware.

Journal of neural engineering Dec 21, 2018
OBJECTIVE: The objective of this work is to use the capability of spiking neural networks to capture the spatio-temporal information encoded in time-series signals and decode them without the use of hand-crafted features and vector-based learning and...
Models, Neurological Hand Strength Electroencephalography Humans Prostheses and Implants Neural Networks, Computer Prosthesis Design Hand Female Electromyography Machine Learning Young Adult Algorithms Male
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From research to clinic: A sensor reduction method for high-density EEG neurofeedback systems.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Dec 16, 2018
OBJECTIVE: To accurately deliver a source-estimated neurofeedback (NF) signal developed on a 128-sensors EEG system on a reduced 32-sensors EEG system.
Humans Electroencephalography Monte Carlo Method Brain Neurofeedback
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Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework.

Computational intelligence and neuroscience Dec 13, 2018
Fusing multichannel neurophysiological signals to recognize human emotion states becomes increasingly attractive. The conventional methods ignore the complementarity between time domain characteristics, frequency domain characteristics, and time-freq...
Arousal Humans Algorithms Deep Learning Support Vector Machine Emotions Electroencephalography
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Editorial. Pediatric stereoelectroencephalography: review of a cohort.

Journal of neurosurgery. Pediatrics Dec 7, 2018
Electroencephalography Stereotaxic Techniques Cohort Studies Child Robotics Humans
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Automatic seizure detection using three-dimensional CNN based on multi-channel EEG.

BMC medical informatics and decision making Dec 7, 2018
BACKGROUND: Automated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain ...
Epilepsy Seizures Signal Processing, Computer-Assisted Neural Networks, Computer Machine Learning Electroencephalography Humans
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A robust and subject-specific sequential forward search method for effective channel selection in brain computer interfaces.

Journal of neuroscience methods Dec 6, 2018
BACKGROUND: The input signals of electroencephalography (EEG) based brain computer interfaces (BCI) are extensively acquired from scalp with a multi-channel system. However, multi-channel signals might contain redundant information and increase compu...
Humans Brain-Computer Interfaces Support Vector Machine Electroencephalography Signal Processing, Computer-Assisted
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