Data Augmentation of SSVEPs Using Source Aliasing Matrix Estimation for Brain–Computer Interfaces

Conclusion: SAME is an effective method for SSVEP-BCIs to augment the calibration data, thereby significantly enhancing the performance of eTRCA and TDCA. Significance: We propose a new data-augmentation method that is compatible with the state-of-the-art algorithms of SSVEP-based BCIs. It can significantly reduce the efforts required to calibrate SSVEP-BCIs, which is promising for the development of practical BCIs.
Source: IEEE Transactions on Biomedical Engineering - Category: Biomedical Engineering Source Type: research