TY - JOUR
T1 - Facilitating Calibration in High-Speed BCI Spellers via Leveraging Cross-Device Shared Latent Responses
AU - Nakanishi, Masaki
AU - Wang, Yu Te
AU - Wei, Chun-Shu
AU - Chiang, Kuan Jung
AU - Jung, Tzyy Ping
PY - 2020/4
Y1 - 2020/4
N2 - 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.
AB - 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.
KW - Brain-computer interfaces
KW - canonical correlation analysis
KW - electroencephalography
KW - steady-state visual evoked potentials
KW - task-related component analysis
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85082342269&partnerID=8YFLogxK
U2 - 10.1109/TBME.2019.2929745
DO - 10.1109/TBME.2019.2929745
M3 - Article
C2 - 31329104
AN - SCOPUS:85082342269
SN - 0018-9294
VL - 67
SP - 1105
EP - 1113
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 4
M1 - 8765815
ER -