Results 31 to 40 of about 3,887,920 (279)

Resting state cortical EEG rhythms in Alzheimer's disease: toward EEG markers for clinical applications: a review [PDF]

, 2012
The human brain contains an intricate network of about 100 billion neurons. Aging of the brain is characterized by a combination of synaptic pruning, loss of cortico-cortical connections, and neuronal apoptosis that provoke an age-dependent decline of ...
Lizio, Roberta, De Vico Fallani, Fabrizio, Fabrizio Vecchio, F. De Vico Fallani, Frisoni, Giovanni, Fabrizio De Vico Fallani, Giulio Verrienti, Giovanni Frisoni, Vecchio, Fabrizio, Rossini, Paolo M, G. Verrienti, P. M. Rossini, F. Vecchio, Verrienti, Giulio, Frisoni, Giovanni B., Paolo M. Rossini, Rossini, Paolo M., Babiloni, Claudio, Fde V. Fallani, Blinowska, Katarzyna, G. Frisoni, Rossini, Paolo Maria, Claudio Babiloni, K. Blinowska, Katarzyna Blinowska, Roberta Lizio   +25 more
core   +1 more source

Modulation of EEG Theta Band Signal Complexity by Music Therapy [Forthcoming] [PDF]

The primary goal of this study was to investigate the impact of monochord (MC) sounds, a type of archaic sounds used in music therapy, on the neural complexity of EEG signals obtained from patients undergoing chemotherapy.
Bhattacharya, Joydeep, Lee, Eun-Jeong
core   +7 more sources

Continuous EEG source imaging enhances analysis of EEG-fMRI in focal epilepsy [PDF]

, 2010
Introduction: EEG-correlated fMRI (EEG-fMRI) studies can reveal haemodynamic changes associated with Interictal Epileptic Discharges (IED). Methodological improvements are needed to increase sensitivity and specificity for localising the epileptogenic ...
Lemieux, L., Michel, C. M., Guye, M., Rodionov, R., Thornton, R., Vulliemoz, Serge, Carmichael, D. W., Lhatoo, S. D., Michel, Christoph, Duncan, J.S., Michel, C.M., Carmichael, D.W., Lhatoo, S.D., Guye, Maxime, Vulliemoz, S., Duncan, J. S.   +15 more
core   +1 more source

An Efficient Method for Classification of Alcoholic and Normal Electroencephalogram Signals Based on Selection of an Appropriate Feature

Journal of Medical Signals and Sensors, 2023
Background: Alcohol addiction contributes to disorders in brain's normal patterns. Analysis of electroencephalogram (EEG) signal helps to diagnose and classify alcoholic and normal EEG signal.
Maryam Dorvashi, Neda Behzadfar, Ghazanfar Shahgholian   +2 more
doaj   +1 more source

Epileptic EEG Signal Classification Using Convolutional Neural Network Based on Multi-Segment of EEG Signal

International Journal of Intelligent Engineering and Systems, 2021
: High performance in the epileptic electroencephalogram (EEG) signal classification is an important step in diagnosing epilepsy. Furthermore, this classification is carried out to determine whether the EEG signal from a person's examination results is ...
Irwan Budi Santoso, Yudhi Adrianto, A. Sensusiati, D. Wulandari, I. Purnama   +4 more
semanticscholar   +1 more source

Multiscale Hjorth Descriptor on Epileptic EEG Classification

Journal of Electrical and Computer Engineering, 2023
The electroencephalogram (EEG) examination provides information on the brain’s electricity, especially in cases of epilepsy. Since the characteristics of EEG signals are nonlinear and nonstationary, visual inspection becomes very difficult.
Achmad Rizal, Sugondo Hadiyoso, Suci Aulia, Inung Wijayanto, null Triwiyanto, Ziani Said   +5 more
doaj   +1 more source

An end-to-end deep learning approach to MI-EEG signal classification for BCIs

Expert systems with applications, 2018
Goal: To develop and implement a Deep Learning (DL) approach for an electroencephalogram (EEG) based Motor Imagery (MI) Brain-Computer Interface (BCI) system that could potentially be used to improve the current stroke rehabilitation strategies.
Hauke Dose, Jakob S. Møller, H. Iversen, S. Puthusserypady   +3 more
semanticscholar   +1 more source

EEG signal classification using wavelet feature extraction and neural networks [PDF]

, 2006
Decision support systems have been utilised since 1960, providing physicians with fast and accurate means towards more accurate diagnoses and increased tolerance when handling missing or incomplete data.
Vassilis Kodogiannis, Kenneth Revett, Jahankhani, P., Revett, K., Pari Jahankhani, Kodogiannis, V.   +5 more
core   +1 more source

Deep learning-based self-induced emotion recognition using EEG

Frontiers in Neuroscience, 2022
Emotion recognition from electroencephalogram (EEG) signals requires accurate and efficient signal processing and feature extraction. Deep learning technology has enabled the automatic extraction of raw EEG signal features that contribute to classifying ...
Yerim Ji, Suh-Yeon Dong
doaj   +1 more source

Real time eye blink noise removal from EEG signals using morphological component analysis [PDF]

, 2013
This paper presents a method of removing the noise caused by eye blinks from an electroencephalogram (EEG) signal in real time based on morphological component analysis (MCA).
Beeby, Stephen, Matiko, Joseph W., Tudor, John   +2 more
core   +2 more sources