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    一种新的基于变压器的方法使用功能近红外光谱 (fNIRS) 改进了使用更少通道的跨主题心理工作负载分类. 这种方法提高了真实世界的神经康复和技能培训应用的准确性.

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    科学领域:

    • 神经科学是一个神经科学.
    • 生物医学工程 生物医学工程
    • 机器学习 机器学习

    背景情况:

    • 心理状态监测对于神经康复和技能培训至关重要.
    • 功能近红外光谱学 (fNIRS) 是一个有前途的工具.
    • 现实世界的应用需要更少的检测通道和强大的跨主题性能的方法.

    研究的目的:

    • 提出一种基于变压器的方法,用于跨主题的心理工作负载分类.
    • 为了实现这一目标,使用有限的功能近红外光谱 (fNIRS) 频道.
    • 提高心理状态监测的效率和准确性.

    主要方法:

    • fNIRS信号被划分为时间补丁,并转化为嵌入.
    • 使用变压器编码器来捕获远程依赖.
    • 一个多层感知子 (MLP) 头处理了输出进行分类.

    主要成果:

    • 与以前的方法相比,拟议的基于变压器的方法显示出更高的跨学科分类准确性.
    • 该方法实现了相对高效的计算.
    • 在开放访问的fNIRS2MW数据集上进行了验证.

    结论:

    • 基于变压器的方法对于使用fNIRS进行跨主题心理工作负载分类是有效的.
    • 这种方法为现实世界的心理状态监测提供了一个有希望的解决方案.
    • 这项技术显示了促进神经康复和技能培训的潜力.