[HTML][HTML] EEG signal classification using PSO trained RBF neural network for epilepsy identification
SK Satapathy, S Dehuri, AK Jagadev - Informatics in Medicine Unlocked, 2017 - Elsevier
The electroencephalogram (EEG) is a low amplitude signal generated in the brain, as a
result of information flow during the communication of several neurons. Hence, careful …
result of information flow during the communication of several neurons. Hence, careful …
A Robust Machine Learning Model for Prediction: The Electroencephalography
R Bajaj, C Chaudhary, H Bhardwaj… - … on System Modeling …, 2022 - ieeexplore.ieee.org
A typical time series classification issue that has recently received a lot of attention is eye
state identification. To classify the states of the eyes, electroencephalography (EEG), a …
state identification. To classify the states of the eyes, electroencephalography (EEG), a …
Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees
The analysis of Electroencephalography (EEG) signals has been an effective way of eye
state identification. Its significance is highlighted by studies that examined the classification …
state identification. Its significance is highlighted by studies that examined the classification …
Eye State Identification Utilizing EEG Signals: A Combined Method Using Self‐Organizing Map and Deep Belief Network
Measuring brain activity through Electroencephalogram (EEG) analysis for eye state
prediction has attracted attention from machine learning researchers. There have been …
prediction has attracted attention from machine learning researchers. There have been …
Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson's Disease
M Belyaev, M Murugappan, A Velichko, D Korzun - Sensors, 2023 - mdpi.com
This study presents the concept of a computationally efficient machine learning (ML) model
for diagnosing and monitoring Parkinson's disease (PD) using rest-state EEG signals (rs …
for diagnosing and monitoring Parkinson's disease (PD) using rest-state EEG signals (rs …
[PDF][PDF] EEG Based Eye State Classification using Deep Belief Network and Stacked AutoEncoder.
A Brain-Computer Interface (BCI) provides an alternative communication interface between
the human brain and a computer. The Electroencephalogram (EEG) signals are acquired …
the human brain and a computer. The Electroencephalogram (EEG) signals are acquired …
The classification of eye state by using kNN and MLP classification models according to the EEG signals
What is widely used for classification of eye state to detect human's cognition state is
electroencephalography (EEG). In this study, the usage of EEG signals for online eye state …
electroencephalography (EEG). In this study, the usage of EEG signals for online eye state …
Hybrid classification model for eye state detection using electroencephalogram signals
The electroencephalography (EEG) signal is an essential source of Brain–Computer
Interface (BCI) technology implementation. The BCI is nothing but a non-muscle …
Interface (BCI) technology implementation. The BCI is nothing but a non-muscle …
An efficient, ensemble-based classification framework for big medical data
Fetching useful information from big medical datasets is a complicated task in the big data
age. Various classification algorithms are used in the data mining process to analyze …
age. Various classification algorithms are used in the data mining process to analyze …
A comparative study of machine learning algorithms for physiological signal classification
The present work aims at the evaluation of the effectiveness of different machine learning
algorithms on a variety of clinical data, derived from small, medium, and large publicly …
algorithms on a variety of clinical data, derived from small, medium, and large publicly …