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

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...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Ximing Mai, Jikun Ai, Yuxuan Wei, Xiangyang Zhu, Jianjun Meng
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	IEEE 2023-01-01
Sarja:	IEEE Transactions on Neural Systems and Rehabilitation Engineering
Aiheet:	Brain-computer interfaces (BCIs) data augmentation phase-locked time-shift (PLTS) steady-state visual evoked potential (SSVEP)
Linkit:	https://ieeexplore.ieee.org/document/10275122/
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