期刊
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
卷 31, 期 -, 页码 4096-4105出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2023.3323351
关键词
Training; Training data; Spatial filters; Calibration; Visualization; Filtering algorithms; Data models; Brain-computer interfaces (BCIs); data augmentation; phase-locked time-shift (PLTS); steady-state visual evoked potential (SSVEP)
This study proposes a novel augmentation method (PLTS) for SSVEP-BCI, which significantly improves the classification performance and ITR of SSVEP algorithms. This method enhances the practicality of SSVEP-based brain spellers under limited training data.
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 ${40.88}\pm {4.54}$ bits/min to ${122.61}\pm {7.05}$ 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.
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