Results 21 to 30 of about 236,683 (245)

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

Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer's disease patients [PDF]

, 2008
We analysed the electroencephalogram (EEG) from Alzheimer's disease (AD) patients with two nonlinear methods: approximate entropy (ApEn) and auto mutual information (AMI).
Gomez, C., Escudero, J.; id_orcid, Espino, P., Hornero, R., Abasolo, D.   +4 more
core   +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

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

Detection of intention level in response to task difficulty from EEG signals [PDF]

, 2013
We present an approach that enables detecting intention levels of subjects in response to task difficulty utilizing an electroencephalogram (EEG) based brain-computer interface (BCI).
Elif Hocaoglu, Çetin, Müjdat, Volkan Patoglu, Patoğlu, Volkan, Mujdat Cetin, Koyaş, Ela, Hocaoğlu, Elif, Eia Koyas   +7 more
core   +1 more source

Electroencephalogram (EEG) Based Prediction of Attention Deficit Hyperactivity Disorder (ADHD) Using Machine Learning [PDF]

Eur Psychiatry
Jun Won Kim,1 Bung-Nyun Kim,2 Johanna Inhyang Kim,3 Chan-Mo Yang,4 Jaehyung Kwon5 1Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea; 2Division of Child and Adolescent Psychiatry, Department of Psychiatry ...
Kim J, Yang S.
europepmc   +3 more sources

Sleep characteristics and stages detection and analysis using electroencephalogram (EEG) [PDF]

, 2021
An electroencephalogram (EEG) signal is an efficient tool for identifying and diagnosing neurological diseases. In addition, it is very important for assisting patients with a disability to interact with their environment through a brain-computer ...
Al-Salman, Wessam Abbas Hamed
core   +1 more source

Artifact reduction in multichannel pervasive EEG using hybrid WPT-ICA and WPT-EMD signal decomposition techniques [PDF]

, 2014
In order to reduce the muscle artifacts in multi-channel pervasive Electroencephalogram (EEG) signals, we here propose and compare two hybrid algorithms by combining the concept of wavelet packet transform (WPT), empirical mode decomposition (EMD) and ...
Koushik Maharatna, Das, Saptarshi, Saptarshi Das, Maharatna, Koushik, Valentina Bono, Bono, Valentina, Wasifa Jamal, Jamal, Wasifa   +7 more
core   +1 more source

PIN generation using EEG : a stability study [PDF]

, 2014
In a previous study, it has been shown that brain activity, i.e. electroencephalogram (EEG) signals, can be used to generate personal identification number (PIN). The method was based on brain–computer interface (BCI) technology using a P300-based BCI
Revett, Kenneth, Palaniappan, Ramaswamy
core   +1 more source

Nonlinear Electroencephalogram (EEG) Analysis in Sleep Medicine

Journal of Sleep Sciences, 2021
There are three main views in computational neuroscience including deterministic, stochastic, and nonlinear approaches. In the deterministic approach, the human brain is considered a linear and stationary system with determined parameters.
Mortaza Zangeneh Soroush
doaj   +1 more source