TELKOMNIKA Telecommunication Computing Electronics and Control
Automatic channel selection using shuffled frog leaping algorithm for EEG based addiction detection
Dublin Core
Title
TELKOMNIKA Telecommunication Computing Electronics and Control
Automatic channel selection using shuffled frog leaping algorithm for EEG based addiction detection
Automatic channel selection using shuffled frog leaping algorithm for EEG based addiction detection
Subject
Automatic channel selection
Drug addiction
Electroencephalography
MLP with SFLA
Shuffled frog leaping algorithm
Drug addiction
Electroencephalography
MLP with SFLA
Shuffled frog leaping algorithm
Description
Drug addiction is a complex neurobiological disorder that necessitates
comprehensive treatment of both the body and mind. It is categorized as a
brain disorder due to its impact on the brain. Various methods such as
electroencephalography (EEG), functional magnetic resonance imaging
(FMRI), and magnetoencephalography (MEG) can capture brain activities
and structures. EEG signals provide valuable insights into neurological
disorders, including drug addiction. Accurate classification of drug addiction
from EEG signals relies on appropriate features and channel selection.
Choosing the right EEG channels is essential to reduce computational costs
and mitigate the risk of overfitting associated with using all available
channels. To address the challenge of optimal channel selection in addiction
detection from EEG signals, this work employs the shuffled frog leaping
algorithm (SFLA). SFLA facilitates the selection of appropriate channels,
leading to improved accuracy. Wavelet features extracted from the selected
input channel signals are then analyzed using various machine learning
classifiers to detect addiction. Experimental results indicate that after
selecting features from the appropriate channels, classification accuracy
significantly increased across all classifiers. Particularly, the multi-layer
perceptron (MLP) classifier combined with SFLA demonstrated a
remarkable accuracy improvement of 15.78% while reducing time
complexity.
comprehensive treatment of both the body and mind. It is categorized as a
brain disorder due to its impact on the brain. Various methods such as
electroencephalography (EEG), functional magnetic resonance imaging
(FMRI), and magnetoencephalography (MEG) can capture brain activities
and structures. EEG signals provide valuable insights into neurological
disorders, including drug addiction. Accurate classification of drug addiction
from EEG signals relies on appropriate features and channel selection.
Choosing the right EEG channels is essential to reduce computational costs
and mitigate the risk of overfitting associated with using all available
channels. To address the challenge of optimal channel selection in addiction
detection from EEG signals, this work employs the shuffled frog leaping
algorithm (SFLA). SFLA facilitates the selection of appropriate channels,
leading to improved accuracy. Wavelet features extracted from the selected
input channel signals are then analyzed using various machine learning
classifiers to detect addiction. Experimental results indicate that after
selecting features from the appropriate channels, classification accuracy
significantly increased across all classifiers. Particularly, the multi-layer
perceptron (MLP) classifier combined with SFLA demonstrated a
remarkable accuracy improvement of 15.78% while reducing time
complexity.
Creator
Grace Mary Kanaga Edward, Angela Esther Rajakumar, Kumudha Raimond, Anitha Jeevanayagam
Source
http://telkomnika.uad.ac.id
Date
Apr 30, 2023
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Grace Mary Kanaga Edward, Angela Esther Rajakumar, Kumudha Raimond, Anitha Jeevanayagam, “TELKOMNIKA Telecommunication Computing Electronics and Control
Automatic channel selection using shuffled frog leaping algorithm for EEG based addiction detection,” Repository Horizon University Indonesia, accessed November 21, 2024, https://repository.horizon.ac.id/items/show/4594.
Automatic channel selection using shuffled frog leaping algorithm for EEG based addiction detection,” Repository Horizon University Indonesia, accessed November 21, 2024, https://repository.horizon.ac.id/items/show/4594.