自然和合成噪声数据增强对通过脑计算机接口和深度学习对物理动作分类的影响
Yuri Gordienko1, Nikita Gordienko1, Vladyslav Taran1
1Computer Engineering Department, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute," Kyiv, Ukraine.
Frontiers in neuroinformatics
|March 14, 2025
概括
深度神经网络 (DNN) 有效地将脑电图 (EEG) 信号分类为物理行为. 自然噪声数据增强比合成方法更提高了分类准确性,对边缘计算应用显示出希望.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 大脑计算机接口 (BCI) 使用脑电图 (EEG) 信号.
- 深度神经网络 (DNN) 显示了分类时间序列数据的潜力,比如物理行为 (PA).
研究的目的:
- 用一个简单的DNN与完全连接网络 (FCN) 和卷积神经网络 (CNN) 组件来分类手指-手掌-手操纵.
- 通过两种类型的噪声数据增强 (NDA) 来调查环境影响:自然和合成.
- 为了分析EEG信号的复杂性,使用分离波动分析 (DFA).
主要方法:
- 使用DNN (FCN和CNN) 的抓取和提升 (GAL) 数据集的分类.
- 实现自然NDA (增加样本大小N和偏移值) 和合成NDA (高斯噪声).
- 应用偏差波动分析 (DFA) 来计算波动变量的赫斯特指数 (H).
主要成果:
- 自然NDA,特别是增加样本大小 (N),与合成NDA相比,产生了更高的曲线下面积 (AUC) 值.
- 赫斯特指数 (H) 值在较短的EEG时间窗口 (<2秒) 中高于较长的时间窗口,这表明较短的片段的复杂性更大.
- 该研究使用了低资源的DNN,表明它适合于边缘计算.
结论:
- 自然NDA在提高EEG信号分类准确性方面比合成NDA更有效.
- 较短的EEG信号碎片表现出更高的复杂性,以更高的赫斯特指数为特征.
- 开发的DNN方法是资源高效的,适合在边缘计算设备上部署.
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