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IEEE Trans Biomed Eng. 2019 Jul 18. doi: 10.1109/TBME.2019.2929745. [Epub ahead of print]

Facilitating Calibration in High-Speed BCI Spellers via Leveraging Cross-Device Shared Latent Responses.



This study proposes a novel device-to-device transfer-learning algorithm for reducing the calibration cost in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) speller by leveraging electroencephalographic (EEG) data previously acquired by different EEG systems.


The transferring is done by projecting the scalp-channel EEG signals onto a shared latent domain across devices. Three spatial filtering techniques including channel averaging, canonical correlation analysis (CCA), and task-related component analysis (TRCA) were employed to extract the shared responses from different devices. The transferred data were integrated into a template-matching-based algorithm to detect SSVEPs. To evaluate its transferability, this study conducted two sessions of simulated online BCI experiments with ten subjects using 40 visual stimuli modulated by joint frequency-phase coding method. In each session, two different EEG devices were used: 1) the Quick-30 system (Cognionics, Inc.) with dry electrodes and 2) the ActiveTwo system (BioSemi, Inc.) with wet electrodes.


The proposed method with CCA- and TRCA-based spatial filters achieved significantly higher classification accuracy compared with the calibration-free standard CCA-based method.


This study validated the feasibility and effectiveness of the proposed method in implementing calibration-free SSVEP-based BCIs.


The proposed method has great potentials to enhance practicability and usability of real-world SSVEP-based BCI applications by leveraging user-specific data recorded in previous sessions even with different EEG systems and montages.


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