AIMC Topic: Electroencephalography
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EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes.
IEEE transactions on medical imaging
Dec 10, 2019
Epilepsy is a neurological disorder characterized by sudden and unpredictable epileptic seizures, which incurs significant negative impacts on patients' physical, psychological and social health. A practical approach to assist with the clinical asses...
A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method.
Medical hypotheses
Dec 10, 2019
Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine learning methods have been proposed to develop automated disease diagnosis ...
Robot-assisted versus manual navigated stereoelectroencephalography in adult medically-refractory epilepsy patients.
Epilepsy research
Dec 9, 2019
OBJECTIVE: Stereoelectroencephalography (SEEG) has experienced a recent growth in adoption for epileptogenic zone (EZ) localization. Advances in robotics have the potential to improve the efficiency and safety of this intracranial seizure monitoring ...
A Bayesian Shared Control Approach for Wheelchair Robot With Brain Machine Interface.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Dec 6, 2019
To enhance the performance of the brain-actuated robot system, a novel shared controller based on Bayesian approach is proposed for intelligently combining robot automatic control and brain-actuated control, which takes into account the uncertainty o...
Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network-Feasibility Study.
Sensors (Basel, Switzerland)
Dec 3, 2019
Electroencephalography (EEG) has relatively poor spatial resolution and may yield incorrect brain dynamics and distort topography; thus, high-density EEG systems are necessary for better analysis. Conventional methods have been proposed to solve thes...
Deep Learning Approach for Epileptic Focus Localization.
IEEE transactions on biomedical circuits and systems
Dec 2, 2019
The task of epileptic focus localization receives great attention due to its role in an effective epileptic surgery. The clinicians highly depend on the intracranial EEG data to make a surgical decision related to epileptic subjects suffering from un...
A hybrid self-attention deep learning framework for multivariate sleep stage classification.
BMC bioinformatics
Dec 2, 2019
BACKGROUND: Sleep is a complex and dynamic biological process characterized by different sleep patterns. Comprehensive sleep monitoring and analysis using multivariate polysomnography (PSG) records has achieved significant efforts to prevent sleep-re...
Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture.
Neural networks : the official journal of the International Neural Network Society
Nov 30, 2019
A deep learning classifier for detecting seizures in neonates is proposed. This architecture is designed to detect seizure events from raw electroencephalogram (EEG) signals as opposed to the state-of-the-art hand engineered feature-based representat...
Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision.
Computational intelligence and neuroscience
Nov 27, 2019
In this paper, we evaluate a semiautonomous brain-computer interface (BCI) for manipulation tasks. In such a system, the user controls a robotic arm through motor imagery commands. In traditional process-control BCI systems, the user has to provide t...
Imagination
Models, Theoretical
Event-Related Potentials, P300
Female
Robotics
Signal Processing, Computer-Assisted
Motor Activity
Humans
Electroencephalography
Young Adult
Goals
Mental Fatigue
Artificial Intelligence
Biomechanical Phenomena
Brain-Computer Interfaces
Brain
Male
Proof of Concept Study
Improved Transductive Support Vector Machine for a Small Labelled Set in Motor Imagery-Based Brain-Computer Interface.
Computational intelligence and neuroscience
Nov 25, 2019
Long and tedious calibration time hinders the development of motor imagery- (MI-) based brain-computer interface (BCI). To tackle this problem, we use a limited labelled set and a relatively large unlabelled set from the same subject for training bas...
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