Phase-Locked Time-Shift Data Augmentation Method for SSVEP Brain-Computer Interfaces

• Main Authors: Ximing Mai (Author), Jikun Ai (Author), Yuxuan Wei (Author), Xiangyang Zhu (Author), Jianjun Meng (Author)
• Format: Book
• Published: IEEE, 2023-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1558-0210; 10.1109/TNSRE.2023.3323351
• Keywords: Brain-computer interfaces (BCIs); data augmentation; phase-locked time-shift (PLTS); steady-state visual evoked potential (SSVEP); Medical technology; R855-855.5; Therapeutics. Pharmacology; RM1-950
• Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 4096-4105 (2023)

Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) have achieved an information transfer rate (ITR) of over 300 bits/min, but abundant training data is required. The performance of SSVEP algorithms deteriorates greatly under limited data, and the existing time-shift data augmentation method fails to improve it because the phase-locked requirement between training samples is violated. To address this issue, this study proposes a novel augmentation method, namely phase-locked time-shift (PLTS), for SSVEP-BCI. The similarity between epochs at different time moments was evaluated, and a unique time-shift step was calculated for each class to augment additional data epochs in each trial. The results showed that the PLTS significantly improved the classification performance of SSVEP algorithms on the BETA SSVEP datasets. Moreover, under the condition of one calibration block, by slightly prolonging the calibration duration (from 48 s to 51.5 s), the ITR increased from <inline-formula> <tex-math notation="LaTeX">${40.88}\pm {4.54}$ </tex-math></inline-formula> bits/min to <inline-formula> <tex-math notation="LaTeX">${122.61}\pm {7.05}$ </tex-math></inline-formula> bits/min with the PLTS. This study provides a new perspective on augmenting data epochs for training-based SSVEP-BCI, promotes the classification accuracy and ITR under limited training data, and thus facilitates the real-life applications of SSVEP-based brain spellers.