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

Masaki Nakanishi*, Yu Te Wang, Chun-Shu Wei, Kuan Jung Chiang, Tzyy Ping Jung

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Objective: This paper 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. Methods: 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 paper 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: first, the Quick-30 system (Cognionics, Inc.) with dry electrodes, and second, the ActiveTwo system (BioSemi, Inc.) with wet electrodes. Results: The proposed method with CCA- and TRCA-based spatial filters achieved significantly higher classification accuracy compared with the calibration-free standard CCA-based method. Conclusion: This paper validated the feasibility and effectiveness of the proposed method in implementing calibration-free SSVEP-based BCIs. Significance: 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.

Original languageEnglish
Article number8765815
Pages (from-to)1105-1113
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume67
Issue number4
DOIs
StatePublished - Apr 2020

Keywords

  • Brain-computer interfaces
  • canonical correlation analysis
  • electroencephalography
  • steady-state visual evoked potentials
  • task-related component analysis
  • transfer learning

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