Results 11 to 20 of about 100,182 (299)

Sponge EEG is equivalent regarding signal quality, but faster than routine EEG

Clinical Neurophysiology Practice, 2023
Objective: Emergency diagnostics, such as acquisition of an electroencephalogram (EEG), are of great diagnostic importance, but there is often a lack of experienced personnel. Wet active electrode sponge-based electroencephalogram (sp-EEG) systems can be
Michael Günther, Leonie Schuster, Christian Boßelmann, Holger Lerche, Ulf Ziemann, Katharina Feil, Justus Marquetand   +6 more
doaj   +1 more source

An improved model using convolutional sliding window-attention network for motor imagery EEG classification

Frontiers in Neuroscience, 2023
IntroductionThe classification model of motor imagery-based electroencephalogram (MI-EEG) is a new human-computer interface pattern and a new neural rehabilitation assessment method for diseases such as Parkinson's and stroke.
Yuxuan Huang, Jianxu Zheng, Binxing Xu, Xuhang Li, Yu Liu, Zijian Wang, Hua Feng, Shiqi Cao   +7 more
doaj   +1 more source

Electroencephalogram Signal Eye Blink Rejection Improvement Based on the Hybrid Stone Blind Origin Separation and Particle Swarm Optimization Technique

IEEE Access, 2020
Electroencephalogram (EEG) extraction has widely used Stone's Blind Source Separation (Stone's BSS) algorithm. However, Stone's BSS algorithm is sensitive to the initial half-life (hlong, hshort) and weight vector W parameters, which affect the ...
Mohammed Ali Ahmed, Qi Deyu, Ebtesam N. Alshemmary   +2 more
doaj   +1 more source

Motor imagery signal classification using Wavelet packet decomposition and modified binary grey wolf optimization

Measurement: Sensors, 2022
Nowadays, Electroencephalogram (EEG) signals are widely used in brain-computer interfaces (BCIs), including the identification of motor imagery (MI) activities and prostheses.
Pawan, Rohtash Dhiman
doaj   +1 more source

A Deep Learning Model for Stroke Patients’ Motor Function Prediction

Applied Bionics and Biomechanics, 2022
Deep learning models are effectively employed to transfer learning to adopt learning from other areas. This research utilizes several neural structures to interpret the electroencephalogram images (EEG) of brain-injured cases to plan operative imagery ...
Abeer Abdulaziz AlArfaj, Hanan A. Hosni Mahmoud, Alaaeldin M. Hafez   +2 more
doaj   +1 more source

Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning

Sensors, 2023
Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person’s psychological and physical health.
Yanting Xu, Hongyang Zhong, Shangyan Ying, Wei Liu, Guibin Chen, Xiaodong Luo, Gang Li   +6 more
doaj   +1 more source

Novel Early EEG Measures Predicting Brain Recovery after Cardiac Arrest

Entropy, 2017
In this paper, we propose novel quantitative electroencephalogram (qEEG) measures by exploiting three critical and distinct phases (isoelectric, fast progression, and slow progression) of qEEG time evolution.
Kab-Mun Cha, Nitish V. Thakor, Hyun-Chool Shin   +2 more
doaj   +1 more source

The Effect of Time Window Length on EEG-Based Emotion Recognition

Sensors, 2022
Various lengths of time window have been used in feature extraction for electroencephalogram (EEG) signal processing in previous studies. However, the effect of time window length on feature extraction for the downstream tasks such as emotion recognition
Delin Ouyang, Yufei Yuan, Guofa Li, Zizheng Guo   +3 more
doaj   +1 more source

On-Off Intermittency in Time Series of Spontaneous Paroxysmal Activity in Rats with Genetic Absence Epilepsy [PDF]

, 2006
Dynamic behavior of complex neuronal ensembles is a topic comprising a streamline of current researches worldwide. In this article we study the behavior manifested by epileptic brain, in the case of spontaneous non-convulsive paroxysmal activity.
Alexander Hramov, Alexey A. Koronovskii, Berge P., C. M. van Rijn, E. Sitnikova, I. S. Midzyanovskaya, Kaiser G., Koronovskii A. A., Paxinos G., Schuster H. G., Torresani B.   +10 more
core   +3 more sources

EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals [PDF]

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021
8 pages, 8 figures, under review in EMBC ...
Andac Demir, Toshiaki Koike-Akino, Ye Wang 0001, Masaki Haruna, Deniz Erdogmus   +4 more
openaire   +3 more sources