<ul><li>Machine learning and deep learning advancements in Brain-Computer Interface (BCI) have limitations due to individual health, hardware variations, and cultural differences affecting neural data.</li><li>Transfer learning with active sampling (AS) using a convolutional neural network enhances BCI performance in diverse settings.</li><li>The proposed AS method improves classification accuracy by 5.36% and reduces standard deviation by 12.22%.</li><li>This approach shows better generalizability, computational time, and training efficiency compared to traditional methods.</li></ul>

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data

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