AIMC Topic: Electroencephalography

Clear Filters Showing 1721 to 1730 of 2295 articles

Human interaction with robotic systems: performance and workload evaluations.

Ergonomics
We first tested the effect of differing tactile informational forms (i.e. directional cues vs. static cues vs. dynamic cues) on objective performance and perceived workload in a collaborative human-robot task. A second experiment evaluated the influe...

Phenomenological network models: Lessons for epilepsy surgery.

Epilepsia
The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with recent developments in epilepsy research, where epilepsy is seen as a network disease. Computational m...

EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease.

Scientific reports
Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electr...

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks.

IEEE journal of biomedical and health informatics
Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of h...

Feature selection before EEG classification supports the diagnosis of Alzheimer's disease.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among each other. Thus, feature selection (FS) algorithms are often considered to find relevant/non-redunda...

A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).

Medical & biological engineering & computing
Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning fra...

The impact of goal-oriented task design on neurofeedback learning for brain-computer interface control.

Medical & biological engineering & computing
Neurofeedback training teaches individuals to modulate brain activity by providing real-time feedback and can be used for brain-computer interface control. The present study aimed to optimize training by maximizing engagement through goal-oriented ta...

Synaptic damage underlies EEG abnormalities in postanoxic encephalopathy: A computational study.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: In postanoxic coma, EEG patterns indicate the severity of encephalopathy and typically evolve in time. We aim to improve the understanding of pathophysiological mechanisms underlying these EEG abnormalities.

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

IEEE journal of biomedical and health informatics
Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applicati...

Time-Varying System Identification Using an Ultra-Orthogonal Forward Regression and Multiwavelet Basis Functions With Applications to EEG.

IEEE transactions on neural networks and learning systems
A new parametric approach is proposed for nonlinear and nonstationary system identification based on a time-varying nonlinear autoregressive with exogenous input (TV-NARX) model. The TV coefficients of the TV-NARX model are expanded using multiwavele...