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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Shiraz University of Medical Sciences
2024-12-01
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| Series: | Journal of Biomedical Physics and Engineering |
| Subjects: | |
| Online Access: | https://jbpe.sums.ac.ir/article_49426_8997ec3b5f672cae22b6c98a7b875eb2.pdf |
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