AIMC Topic: Electroencephalography
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Developing a Three- to Six-State EEG-Based Brain-Computer Interface for a Virtual Robotic Manipulator Control.
IEEE transactions on bio-medical engineering
Aug 17, 2018
OBJECTIVE: We develop an electroencephalography (EEG)-based noninvasive brain-computer interface (BCI) system having short training time (15 min) that can be applied for high-performance control of robotic prosthetic systems.
Robot-guided pediatric stereoelectroencephalography: single-institution experience.
Journal of neurosurgery. Pediatrics
Aug 17, 2018
OBJECTIVEStereoelectroencephalography (SEEG) has increased in popularity for localization of epileptogenic zones in drug-resistant epilepsy because safety, accuracy, and efficacy have been well established in both adult and pediatric populations. Dev...
Deep learning applied to whole-brain connectome to determine seizure control after epilepsy surgery.
Epilepsia
Aug 10, 2018
OBJECTIVE: We evaluated whether deep learning applied to whole-brain presurgical structural connectomes could be used to predict postoperative seizure outcome more accurately than inference from clinical variables in patients with mesial temporal lob...
High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence.
Computational intelligence and neuroscience
Aug 7, 2018
High-frequency oscillations (HFOs) in the electroencephalogram (EEG) are thought to be a promising marker for epileptogenicity. A number of automated detection algorithms have been developed for reliable analysis of invasively recorded HFOs. However,...
Modeling brain dynamic state changes with adaptive mixture independent component analysis.
NeuroImage
Aug 4, 2018
There is a growing interest in neuroscience in assessing the continuous, endogenous, and nonstationary dynamics of brain network activity supporting the fluidity of human cognition and behavior. This non-stationarity may involve ever-changing formati...
A New Approach on HCI Extracting Conscious Jaw Movements Based on EEG Signals Using Machine Learnings.
Journal of medical systems
Aug 4, 2018
Machine computer interfaces (MCI) are assistive technologies enabling paralyzed peoples to control and communicate their environments. This study aims to discover and represents a new approach on MCI using left/right motions of voluntary jaw movement...
Machine learning as a new paradigm for characterizing localization and lateralization of neuropsychological test data in temporal lobe epilepsy.
Epilepsy & behavior : E&B
Aug 3, 2018
In this study, we employed a kernel support vector machine to predict epilepsy localization and lateralization for patients with a diagnosis of epilepsy (n = 228). We assessed the accuracy to which indices of verbal memory, visual memory, verbal flue...
A glimpse into the future of clinical neurophysiology: Can we use machines to interpret EEG?
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Jul 29, 2018
A hierarchical semi-supervised extreme learning machine method for EEG recognition.
Medical & biological engineering & computing
Jul 28, 2018
Feature extraction and classification is a vital part in motor imagery-based brain-computer interface (BCI) system. Traditional deep learning (DL) methods usually perform better with more labeled training samples. Unfortunately, the labeled samples a...
Complex-valued unsupervised convolutional neural networks for sleep stage classification.
Computer methods and programs in biomedicine
Jul 26, 2018
BACKGROUND AND OBJECTIVE: Despite numerous deep learning methods being developed for automatic sleep stage classification, almost all the models need labeled data. However, obtaining labeled data is a subjective process. Therefore, the labels will be...
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