AIMC Topic: Electroencephalography

Clear Filters Showing 1271 to 1280 of 2295 articles

Pain phenotypes classified by machine learning using electroencephalography features.

NeuroImage
Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects ...

Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks.

Computational and mathematical methods in medicine
In the process of brain-computer interface (BCI), variations across sessions/subjects result in differences in the properties of potential of the brain. This issue may lead to variations in feature distribution of electroencephalogram (EEG) across su...

A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial.

The Lancet. Child & adolescent health
BACKGROUND: Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could ...

Deep learning and feature based medication classifications from EEG in a large clinical data set.

Scientific reports
The amount of freely available human phenotypic data is increasing daily, and yet little is known about the types of inferences or identifying characteristics that could reasonably be drawn from that data using new statistical methods. One data type ...

Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia.

PloS one
Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signal...

The EEG Signal Analysis for Spatial Cognitive Ability Evaluation Based on Multivariate Permutation Conditional Mutual Information-Multi-Spectral Image.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
This study aims to find an effective method to evaluate the efficacy of cognitive training of spatial memory under a virtual reality environment, by classifying the EEG signals of subjects in the early and late stages of spatial cognitive training. T...

Linear predictive coding distinguishes spectral EEG features of Parkinson's disease.

Parkinsonism & related disorders
OBJECTIVE: We have developed and validated a novel EEG-based signal processing approach to distinguish PD and control patients: Linear-predictive-coding EEG Algorithm for PD (LEAPD). This method efficiently encodes EEG time series into features that ...

The Bayesian brain in imbalance: Medial, lateral and descending pathways in tinnitus and pain: A perspective.

Progress in brain research
Tinnitus and pain share similarities in their anatomy, pathophysiology, clinical picture and treatments. Based on what is known in the pain field, a heuristic model can be proposed for the pathophysiolgy of tinnitus. This heuristic pathophysiological...

Emotional EEG classification using connectivity features and convolutional neural networks.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural networks (CNNs) are widely used to recognize the user's state through electroencephalography (EEG) signals. In the previous studies, the EEG signals are usually fed into the CNNs in the form of high-dimensional raw data. However,...

Linear versus deep learning methods for noisy speech separation for EEG-informed attention decoding.

Journal of neural engineering
OBJECTIVE: A hearing aid's noise reduction algorithm cannot infer to which speaker the user intends to listen to. Auditory attention decoding (AAD) algorithms allow to infer this information from neural signals, which leads to the concept of neuro-st...