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iTa-DFiE: An Innovative Tensor Algebra-Based Detection Framework for Incomplete Noninvasive Electroencephalography
- Source :
- IEEE Access, Vol 12, Pp 61717-61740 (2024)
- Publication Year :
- 2024
- Publisher :
- IEEE, 2024.
-
Abstract
- The paper presents a novel recognition framework for incomplete noninvasive Electroencephalography (EEG) signals relying on the recent advances in tensor algebra, named as An Innovative Tensor Algebra-based Detection Framework for Incomplete Noninvasive Electroencephalography (iTa-DFiE). iTa-DFiE is motivated to improve the diagnostic performance by tackling the major problems shared by a variety of noninvasive EEG-based Brain-Computer Interfaces (BCIs) application is tensorial structured time series with occlusions. The aforementioned challenge setting is solved on two major thrusts, including: 1) tensor completion: discovering hidden patterns and learning their evolving trends to offer missing values imputation via improvement of standard Kalman Filter approach and 2) tensor decomposition: extracting essential hidden information from multi aspects data tensor via extending the most well-known tensor factorization Tucker. The effectiveness and efficiency of the proposed tensor-based framework is proved via successfully improving the pattern classification results on two real-world noninvasive EEG-based motor imagery BCI with diverse corrupted data scenarios, especially in occurrence of consecutive missing observations. Strikingly, iTa-DFiE also outperforms the conventional matrices-based methods and the state-of-the-art tensor techniques in terms of missing reconstructions and feature extraction as well.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 12
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.5f6be2d362104c2e8a4744c37ca06049
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2024.3393413