Research on online template update algorithm for brain-computer interface based on rapid serial visual presentation paradigm

doi:10.12267/j.issn.2096-5931.2026.06.001

Abstract

Abstract:

To tackle the non-stationarity of Electroencephalogram (EEG) signals in Rapid Serial Visual Presentation (RSVP) Brain-Computer Interfaces (BCIs) for patients with Amyotrophic Lateral Sclerosis (ALS), we propose an online template updating algorithm based on the Expectation-Maximization (EM) method. This algorithm formulates EEG signals as a combination of templates and trigger matrices, and updates the templates synchronously using maximum likelihood estimation. Experimental results show that the proposed Online-Updating Hierarchical Discriminant Component Analysis (OU-HDCA) algorithm, which combines the online updating scheme and Hierarchical Discriminant Component Analysis (HDCA), outperforms the traditional static HDCA algorithm remarkably in terms of classification accuracy and information transfer rate, with both indicators maintaining a continuous upward trend. The proposed algorithm adapts to the time-varying characteristics of EEG signals, improves the long-term reliability of BCI systems, and can serve as an effective solution for clinical assistive communication.

Key words: BCI, EEG, online update, signal estimation

CLC Number:

YU Zhaoliang, ZHANG Hongxin, YANG Chen. Research on online template update algorithm for brain-computer interface based on rapid serial visual presentation paradigm[J]. Information and Communications Technology and Policy, 2026, 52(6): 2-8.

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URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2026.06.001

http://ictp.caict.ac.cn/EN/Y2026/V52/I6/2

Figures/Tables 3

References 14

[1]	TOLOCHKO C, SHIRYAEVA O, ALEKSEEVA T, et al. Amyotrophic lateral sclerosis: pathophysiological mechanisms and treatment strategies (part 2)[J]. International Journal of Molecular Sciences, 2025, 26(11):5240. doi: 10.3390/ijms26115240 URL
[2]	HARDIMAN O, VAN DEN BERG L H, KIERNAN M C. Clinical diagnosis and management of amyotrophic lateral sclerosis[J]. Nature Reviews Neurology, 2011, 7(11):639-649. doi: 10.1038/nrneurol.2011.153 pmid: 21989247
[3]	LEBEDEV M A, NICOLELIS M A L. Brain-machine interfaces: past, present and future[J]. Trends in Neurosciences, 2006, 29(9):536-546. pmid: 16859758
[4]	ACQUALAGNA L, BLANKERTZ B. Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP)[J]. Clinical Neurophysiology, 2013, 124(5):901-908. doi: 10.1016/j.clinph.2012.12.050 pmid: 23466266
[5]	XU M, ZHANG B, ZHANG L, et al. A decade of rapid serial visual presentation paradigm in brain-computer interface for target detection: current status and trends[J]. IEEE Transactions on Biomedical Engineering, 2026, 73(3):1307-1320. doi: 10.1109/TBME.2025.3603945 URL
[6]	AWASTHI P, LIN T H, BAE J, et al. Validation of a non-invasive, real-time, human-in-the-loop model of intracortical brain-computer interfaces[J]. Journal of Neural Engineering, 2022, 19(5):056038. doi: 10.1088/1741-2552/ac97c3
[7]	GUARNIERI R, MARINO M, BARBAN F, et al. Online EEG artifact removal for BCI applications by adaptive spatial filtering[J]. Journal of Neural Engineering, 2018, 15(5):056009. doi: 10.1088/1741-2552/aacfdf
[8]	LI F, XIA Y, WANG F, et al. Transfer learning algorithm of P300-EEG signal based on XDAWN spatial filter and riemannian geometry classifier[J]. Applied Sciences, 2020, 10(5):1804. doi: 10.3390/app10051804 URL
[9]	LI J, WANG F, HUANG H, et al. A novel semi-supervised meta learning method for subject-transfer brain-computer interface[J]. Neural Networks, 2023, 163:195-204. doi: 10.1016/j.neunet.2023.03.039 pmid: 37062178
[10]	BOUDIAF M A, BENKHERRAT M, DJELEL S, et al. Efficient adaptive learning via partial-update variable step-size LMS for real-time ERP denoising[J]. Applied Sciences, 2025, 15(23):12702. doi: 10.3390/app152312702 URL
[11]	RIVET B, SOULOUMIAC A, ATTINA V, et al. xDAWN algorithm to enhance evoked potentials: application to brain-computer interface[J]. IEEE Transactions on Biomedical Engineering, 2009, 56(8):2035-2043. doi: 10.1109/TBME.2009.2012869 URL
[12]	HUIZENGA H M, DE MUNCK J C, WALDORP L J, et al. Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model[J]. IEEE Transactions on Biomedical Engineering, 2002, 49(6):533-539. pmid: 12046698
[13]	PARRA L, CHRISTOFOROU C, GERSON A, et al. Spatiotemporal linear decoding of brain state[J]. IEEE Signal Processing Magazine, 2008, 25(1):107-115. doi: 10.1109/MSP.2008.4408447 URL
[14]	KüBLER A, NEUMANN N, KAISER J, et al. Brain-computer communication: self-regulation of slow cortical potentials for verbal communication[J]. Archives of Physical Medicine and Rehabilitation, 2001, 82(11):1533-1539. doi: 10.1053/apmr.2001.26621 pmid: 11689972

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