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使用面具编码的新型数据增强方法,用于基于深度学习的异步SSVEP-BCI.

Wenlong Ding, Aiping Liu, Ling Guan

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    概括
    此摘要是机器生成的。

    脑电图面具编码 (EEG-ME) 改进了静态视觉唤起潜能 (SSVEP) 大脑计算机接口 (BCI) 的深度学习模型. 这种数据增强技术减少了过度拟合,提高了分类准确性,使BCI更强大.

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    科学领域:

    • 神经科学是一个神经科学.
    • 计算机科学 计算机科学
    • 生物医学工程 生物医学工程

    背景情况:

    • 深度学习 (DL) 方法对基于稳定状态视觉唤起潜力 (SSVEP) 的脑计算机接口 (BCI) 有效.
    • 有限的脑电图 (EEG) 数据往往导致SSVEP-BCI的DL模型过拟合.
    • 过度装配阻碍了BCI系统的通用化和稳定性.

    研究的目的:

    • 引入EEG面罩编码 (EEG-ME) 作为SSVEP-BCI的数据增强技术.
    • 通过增强特征学习来减轻DL模型中的过拟合.
    • 提高SSVEP-BCI系统的一般化能力.

    主要方法:

    • 通过掩盖部分EEG数据,拟议的EEG面罩编码 (EEG-ME).
    • 在三个DL架构上验证了EEG-ME:CNN-Former,tCNN和EEGNet.
    • 在具有不同时间窗口长度的基准和BETA数据集上评估性能.

    主要成果:

    • 在不同的DL方法和时间窗口中,EEG-ME显著提高了平均分类准确性.
    • 在基准和BETA数据集上,CNN-Former,tCNN和EEGNet的准确性分别增加了3.18%和11.09%.
    • 一秒钟的时间窗口显示了显著的性能增强.

    结论:

    • 在SSVEP-BCI中,EEG-ME有效地提高了DL模型的稳定性和通用性.
    • 拟议的方法促进了异步SSVEP-BCI系统的实际实施.
    • 改进的BCI性能导致更灵活和可靠的人机交互.