A Transfer Learning SSVEP Decoding Algorithm Calibrated With Single-Trial Data

Training-based algorithms significantly outperform training-free methods in terms of recognition performance for steady-state visual-evoked potential (SSVEP)-based brain-computer Interfaces (BCIs). However, collecting training data requires calibration experiments that are effort-intensive and often...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. PP; S. 1 - 15
Hauptverfasser: Jin, Jing, Qin, Ke, Allison, Brendan Z., Li, Shurui, Zhang, Yutao, Wang, Xingyu, Cichocki, Andrzej
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 24.10.2025
Schlagworte:
Brain–computer interfaces (BCIs)
Calibration
Correlation
cross-dataset
data mismatch problem
Decoding
Electroencephalography
Signal to noise ratio
Spatial filters
Steady-state
steady-state visual-evoked potential (SSVEP)
Transfer learning
transfer learning (TL)
Vectors
Visualization
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:Training-based algorithms significantly outperform training-free methods in terms of recognition performance for steady-state visual-evoked potential (SSVEP)-based brain-computer Interfaces (BCIs). However, collecting training data requires calibration experiments that are effort-intensive and often costly. These calibration demands limit the practicality of BCI, as users (and even system operators) may experience fatigue or lose interest in continued use. Transfer learning (TL) offers an effective solution, but it typically relies on either a certain amount of target domain data or extensive source domain data. To address this limitation, we introduce the concept of cross-dataset TL in SSVEP for the first time to extract transfer knowledge from other datasets. During this process, we identified a data mismatch problem that severely compromises the generalizability of transfer knowledge. To overcome this challenge, we propose a TL-SSVEP decoding algorithm calibrated with single-trial data (TL-CSTD). Specifically, we use 2 s of 8 Hz single-trial calibration data from the target domain to obtain matched transfer templates from the source domain. These templates are then corrected to extract holistic and single-period transfer knowledge, which are subsequently employed to construct an efficient TL-SSVEP decoding model for the target subject. Experimental results on three large SSVEP datasets demonstrate that TL-CSTD effectively addresses the data mismatch problem and achieves excellent SSVEP recognition performance using only 2 s of single-trial calibration data, showing its significant application potential and practicality.
Bibliographie:ObjectType-Article-1
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2025.3617508