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Updated: Jan 14, 2026

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
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打破基于深度学习的SSVEP-BCI的性能障碍:一个联合频率相训练策略.

Wenlong Ding1, Xun Chen1, Aiping Liu1

  • 1University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, 230026, CHINA.

Journal of neural engineering
|January 12, 2026
PubMed
概括

本研究介绍了一个联合频率相训练策略 (JFPTS),用于在脑计算机接口 (BCI) 中稳定状态视觉唤起潜能 (SSVEP) 的分类. 通过利用频率和阶段信息,JFPTS增强了深度学习模型,大大提高了SSVEP分类的准确性.

关键词:
大脑与计算机的接口.深度学习是一种深度学习.频率 - 阶段 频率 - 阶段稳态视觉唤起了潜在的潜力.培训战略 培训战略 培训战略

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