.png){kind=icon}
Return
EEGOpt: A performance efficient Bayesian optimization framework for automated EEG signal classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compbiomed.2025.111023.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Proposed EEGOpt, a novel Bayesian framework to automate EEG denoising, feature extraction, and classification. • TPE outperformed CMA-ES, QMC, GP, RS in optimizing EEG classification pipelines. • Achieved 99.63 % accuracy, outperforming EEGNet, DeepConvNet, and ShallowConvNet. • Caching reduced computation time by 74.69 %, improving efficiency by 95 % over EEGNet. • EEGOpt enables dataset-specific optimization of EEG pipelines for BCIs and neuroscience research.
Journal
IF:
6.3
Papers:
8.3K
Citations:
3.3W
Organization
Cited Papers
A Survey on Denoising Techniques of Electroencephalogram Signals Using Wavelet Transform.png)
Signals
OAAI
SignalsIF0
OAAIShareSaveShareSaveShareSaveAutomated Detection of Major Depressive Disorder With EEG Signals: A Time Series Classification Using Deep LearningIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAIShareSaveOptimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary AlgorithmsSENSORS
OAAI
SENSORSIF3.5
OAAIShareSaveEEG Markers of Treatment Resistance in Idiopathic Generalized Epilepsy: From Standard EEG Findings to Advanced Signal AnalysisBIOMEDICINESOAAI
BIOMEDICINESIF3.9
OAAIShareSave
View more