An Unsupervised Feature Extraction Method based on CLSTM-AE for Accurate P300 Classification in Brain-Computer Interface Systems

An Unsupervised Feature Extraction Method based on CLSTM-AE for Accurate P300 Classification in Brain-Computer Interface Systems

Background: The P300 signal, an endogenous component of event-related potentials, is extracted from an electroencephalography signal and employed in Brain-computer Interface (BCI) devices.Objective: The current study aimed to address challenges in extracting useful features from P300 components and...

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Bibliographic Details
Main Authors: Ramin Afrah, Zahra Amini, Rahele Kafieh
Format: Article
Language:English
Published: Shiraz University of Medical Sciences 2024-12-01
Series:Journal of Biomedical Physics and Engineering
Subjects:
brain-computer interfaces
p300
deep learning
classification
Online Access:https://jbpe.sums.ac.ir/article_49426_8997ec3b5f672cae22b6c98a7b875eb2.pdf
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