Dense Poisson Disk Sampling before adaptive fine-tuning improved P300 BCI accuracy by about 5 percentage points and cut training time by 61%, but the evaluation protocol makes the gain unreliable.
Fuzzy temporal convolutional neural networks in p300-based brain–computer interface for smart home interaction,
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Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data
Dense Poisson Disk Sampling before adaptive fine-tuning improved P300 BCI accuracy by about 5 percentage points and cut training time by 61%, but the evaluation protocol makes the gain unreliable.