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Filter bank temporally local multivariate synchronization index for SSVEP-based BCI
- Source :
- BMC Bioinformatics, Vol 25, Iss 1, Pp 1-18 (2024)
- Publication Year :
- 2024
- Publisher :
- BMC, 2024.
-
Abstract
- Abstract Background Multivariate synchronization index (MSI) has been successfully applied for frequency detection in steady state visual evoked potential (SSVEP) based brain–computer interface (BCI) systems. However, the standard MSI algorithm and its variants cannot simultaneously take full advantage of the time-local structure and the harmonic components in SSVEP signals, which are both crucial for frequency detection performance. To overcome the limitation, we propose a novel filter bank temporally local MSI (FBTMSI) algorithm to further improve SSVEP frequency detection accuracy. The method explicitly utilizes the temporal information of signal for covariance matrix estimation and employs filter bank decomposition to exploits SSVEP-related harmonic components. Results We employed the cross-validation strategy on the public Benchmark dataset to optimize the parameters and evaluate the performance of the FBTMSI algorithm. Experimental results show that FBTMSI outperforms the standard MSI, temporally local MSI (TMSI) and filter bank driven MSI (FBMSI) algorithms across multiple experimental settings. In the case of data length of one second, the average accuracy of FBTMSI is 9.85% and 3.15% higher than that of the FBMSI and the TMSI, respectively. Conclusions The promising results demonstrate the effectiveness of the FBTMSI algorithm for frequency recognition and show its potential in SSVEP-based BCI applications.
Details
- Language :
- English
- ISSN :
- 14712105 and 85420344
- Volume :
- 25
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- BMC Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.165bf4fe1ca9485e98f85420344baf76
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s12859-024-05838-y