AIMC Topic: Electroencephalography
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Exploiting the heightened phase synchrony in patients with neuromuscular disease for the establishment of efficient motor imagery BCIs.
Journal of neuroengineering and rehabilitation
Oct 29, 2018
BACKGROUND: Phase synchrony has extensively been studied for understanding neural coordination in health and disease. There are a few studies concerning the implications in the context of BCIs, but its potential for establishing a communication chann...
Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification.
Computational intelligence and neuroscience
Oct 28, 2018
Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most o...
The revised Cerebral Recovery Index improves predictions of neurological outcome after cardiac arrest.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Oct 27, 2018
OBJECTIVE: Analysis of the electroencephalogram (EEG) background pattern helps predicting neurological outcome of comatose patients after cardiac arrest (CA). Visual analysis may not extract all discriminative information. We present predictive value...
Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors.
Journal of medical systems
Oct 27, 2018
Machine learning and data mining approaches are being successfully applied to different fields of life sciences for the past 20 years. Medicine is one of the most suitable application domains for these techniques since they help model diagnostic info...
A Machine Learning Framework for Automatic and Continuous MMN Detection With Preliminary Results for Coma Outcome Prediction.
IEEE journal of biomedical and health informatics
Oct 24, 2018
Mismatch negativity (MMN) is a component of the event-related potential (ERP) that is elicited through an odd-ball paradigm. The existence of the MMN in a coma patient has a good correlation with coma emergence; however, this component can be difficu...
Electroencephalography-based machine learning for cognitive profiling in Parkinson's disease: Preliminary results.
Movement disorders : official journal of the Movement Disorder Society
Oct 21, 2018
BACKGROUND: Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step toward the identification of predictors of cognitive worsening.
Adaptive Neural Control of a Kinematically Redundant Exoskeleton Robot Using Brain-Machine Interfaces.
IEEE transactions on neural networks and learning systems
Oct 19, 2018
In this paper, a closed-loop control has been developed for the exoskeleton robot system based on brain-machine interface (BMI). Adaptive controllers in joint space, a redundancy resolution method at the velocity level, and commands that generated fr...
Seizure forecasting using single robust linear feature as correlation vector of seizure-like events in brain slices preparation in vitro.
Neurological research
Oct 17, 2018
Epilepsy is a neurological disorder affecting 50 million individuals globally. Modern research has inspected the likelihood of forecasting epileptic seizures. Algorithmic investigations are giving promising results for seizure prediction. Though most...
Assistance Robotics and Biosensors.
Sensors (Basel, Switzerland)
Oct 17, 2018
This Special Issue is focused on breakthrough developments in the field of biosensors and current scientific progress in biomedical signal processing. The papers address innovative solutions in assistance robotics based on bioelectrical signals, incl...
Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification.
Sensors (Basel, Switzerland)
Oct 14, 2018
Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this p...
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