AIMC Topic: Electroencephalography
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Recognition of words from brain-generated signals of speech-impaired people: Application of autoencoders as a neural Turing machine controller in deep neural networks.
Neural networks : the official journal of the International Neural Network Society
Sep 9, 2019
There is an essential requirement to support people with speech and communication disabilities. A brain-computer interface using electroencephalography (EEG) is applied to satisfy this requirement. A number of research studies to recognize brain sign...
A LightGBM-Based EEG Analysis Method for Driver Mental States Classification.
Computational intelligence and neuroscience
Sep 9, 2019
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. H...
Automatic detection of epileptic seizure based on approximate entropy, recurrence quantification analysis and convolutional neural networks.
Artificial intelligence in medicine
Sep 7, 2019
Epilepsy is the most common neurological disorder in humans. Electroencephalogram is a prevalent tool for diagnosing the epileptic seizure activity in clinical, which provides valuable information for understanding the physiological mechanisms behind...
Nonconvulsive epileptic seizure monitoring with incremental learning.
Computers in biology and medicine
Sep 6, 2019
Nonconvulsive epileptic seizures (NCSz) and nonconvulsive status epilepticus (NCSE) are two neurological entities associated with increment in morbidity and mortality in critically ill patients. In a previous work, we introduced a method which accura...
World's fastest brain-computer interface: Combining EEG2Code with deep learning.
PloS one
Sep 6, 2019
We present a novel approach based on deep learning for decoding sensory information from non-invasively recorded Electroencephalograms (EEG). It can either be used in a passive Brain-Computer Interface (BCI) to predict properties of a visual stimulus...
Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features.
NeuroImage. Clinical
Sep 5, 2019
BACKGROUND: The development of optimal classification criteria for specific mental disorders which share similar symptoms is an important issue for precise diagnosis. We investigated whether P300 features in both sensor-level and source-level could b...
Bursts with High and Low Load of Epileptiform Spikes Show Context-Dependent Correlations in Epileptic Mice.
eNeuro
Sep 5, 2019
Hypersynchronous network activity is the defining hallmark of epilepsy and manifests in a wide spectrum of phenomena, of which electrographic activity during seizures is only one extreme. The aim of this study was to differentiate between different t...
Cortical Tracking of Surprisal during Continuous Speech Comprehension.
Journal of cognitive neuroscience
Sep 3, 2019
Speech comprehension requires rapid online processing of a continuous acoustic signal to extract structure and meaning. Previous studies on sentence comprehension have found neural correlates of the predictability of a word given its context, as well...
Machine learning applications in epilepsy.
Epilepsia
Sep 3, 2019
Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, lan...
Multi optimized SVM classifiers for motor imagery left and right hand movement identification.
Australasian physical & engineering sciences in medicine
Aug 30, 2019
EEG signal can be a good alternative for disabled persons who cannot perform actions or perform them improperly. Brain computer interface (BCI) is an attractive technology which permits control and interaction with a computer or a machine using EEG s...
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