AIMC Topic: Electroencephalography
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Epileptic Focus Localization Using Discrete Wavelet Transform Based on Interictal Intracranial EEG.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 30, 2016
Over the past decade, with the development of machine learning, discrete wavelet transform (DWT) has been widely used in computer-aided epileptic electroencephalography (EEG) signal analysis as a powerful time-frequency tool. But some important probl...
An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable.
PloS one
Aug 26, 2016
Supervised machine learning methods typically require splitting data into multiple chunks for training, validating, and finally testing classifiers. For finding the best parameters of a classifier, training and validation are usually carried out with...
Robust Wavelet Stabilized 'Footprints of Uncertainty' for Fuzzy System Classifiers to Automatically Detect Sharp Waves in the EEG after Hypoxia Ischemia.
International journal of neural systems
Aug 18, 2016
Currently, there are no developed methods to detect sharp wave transients that exist in the latent phase after hypoxia-ischemia (HI) in the electroencephalogram (EEG) in order to determine if these micro-scale transients are potential biomarkers of H...
A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 17, 2016
Motor imagery classification is an important topic in brain-computer interface (BCI) research that enables the recognition of a subject's intension to, e.g., implement prosthesis control. The brain dynamics of motor imagery are usually measured by el...
Evoked Potentials, Motor
Motor Cortex
Imagination
Brain Mapping
Machine Learning
Pattern Recognition, Automated
Electroencephalography
Humans
Brain-Computer Interfaces
Algorithms
Signal Processing, Computer-Assisted
Neural Networks, Computer
Movement
Reproducibility of Results
Intention
Fourier Analysis
Sensitivity and Specificity
Thought-Controlled Nanoscale Robots in a Living Host.
PloS one
Aug 15, 2016
We report a new type of brain-machine interface enabling a human operator to control nanometer-size robots inside a living animal by brain activity. Recorded EEG patterns are recognized online by an algorithm, which in turn controls the state of an e...
EEG Analysis During Active and Assisted Repetitive Movements: Evidence for Differences in Neural Engagement.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 2, 2016
Two key ingredients of a successful neuro-rehabilitative intervention have been identified as intensive and repetitive training and subject's active participation, which can be coupled in an active robot-assisted training. To exploit these two elemen...
Introduction.
International journal of neural systems
Jul 31, 2016
Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI.
Computational intelligence and neuroscience
Jul 26, 2016
Brain-computer interfaces represent a range of acknowledged technologies that translate brain activity into computer commands. The aim of our research is to develop and evaluate a BCI control application for certain assistive technologies that can be...
Control of Redundant Kinematic Degrees of Freedom in a Closed-Loop Brain-Machine Interface.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jul 21, 2016
Brain-machine interface (BMI) systems use signals acquired from the brain to directly control the movement of an actuator, such as a computer cursor or a robotic arm, with the goal of restoring motor function lost due to injury or disease of the nerv...
Detecting epileptic seizures with electroencephalogram via a context-learning model.
BMC medical informatics and decision making
Jul 21, 2016
BACKGROUND: Epileptic seizure is a serious health problem in the world and there is a huge population suffering from it every year. If an algorithm could automatically detect seizures and deliver the patient therapy or notify the hospital, that would...
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