AIMC Topic: Electroencephalography
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The Role of a Deep-Learning Method for Negation Detection in Patient Cohort Identification from Electroencephalography Reports.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Dec 5, 2018
Detecting negation in biomedical texts entails the automatic identification of negation cues (e.g. "never", "not", "no longer") as well as the scope of these cues. When medical concepts or terms are identified within the scope of a negation cue, thei...
Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm.
Journal of neural engineering
Dec 3, 2018
OBJECTIVE: Recent attempts in developing brain-computer interface (BCI)-controlled robots have shown the potential of this area in the field of assistive robots. However, implementing the process of picking and placing objects using a BCI-controlled ...
Learning Spatial-Spectral-Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Dec 3, 2018
Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task. However, cross-task assessment is indispensable for extending a pre-trained model to new workload ...
Prediction error connectivity: A new method for EEG state analysis.
NeuroImage
Dec 1, 2018
Several models have been proposed to explain brain regional and interregional communication, the majority of them using methods that tap the frequency domain, like spectral coherence. Considering brain interareal communication as binary interactions,...
What does a pain 'biomarker' mean and can a machine be taught to measure pain?
Neuroscience letters
Nov 29, 2018
Artificial intelligence allows machines to predict human faculties such as image and voice recognition. Can machines be taught to measure pain? We argue that the two fundamental requirements for a device with 'pain biomarker' capabilities are hardwar...
Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI.
Journal of neural engineering
Nov 26, 2018
OBJECTIVE: Despite the effective application of deep learning (DL) in brain-computer interface (BCI) systems, the successful execution of this technique, especially for inter-subject classification, in cognitive BCI has not been accomplished yet. In ...
Efficient sleep classification based on entropy features and a support vector machine classifier.
Physiological measurement
Nov 26, 2018
OBJECTIVE: Sleep quality helps to reflect on the physical and mental condition, and efficient sleep stage scoring promises considerable advantages to health care. The aim of this study is to propose a simple and efficient sleep classification method ...
Computer Aided Diagnosis System for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention.
Computer methods and programs in biomedicine
Nov 24, 2018
BACKGROUND AND OBJECTIVE: Computer Aided Diagnosis (CAD) techniques have widely been used in research to detect the neurological abnormalities and improve the consistency of diagnosis and treatment in medicine. In this study, a new CAD system based o...
A multi-context learning approach for EEG epileptic seizure detection.
BMC systems biology
Nov 22, 2018
BACKGROUND: Epilepsy is a neurological disease characterized by unprovoked seizures in the brain. The recent advances in sensor technologies allow researchers to analyze the collected biological records to improve the treatment of epilepsy. Electroen...
Detecting abnormal electroencephalograms using deep convolutional networks.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Nov 17, 2018
OBJECTIVES: Electroencephalography (EEG) is a central part of the medical evaluation for patients with neurological disorders. Training an algorithm to label the EEG normal vs abnormal seems challenging, because of EEG heterogeneity and dependence of...
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