通过并行多分支CNN和GRU识别增强的时空光谱特征
Linlin Wang1, Mingai Li2,3,4, Liyuan Zhang5
1Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
Medical & biological engineering & computing
|June 9, 2023
概括
这项研究引入了一种新的通道重要性 (NCI) 方法,用于运动图像电脑图 (MI-EEG) 的识别. 与PMBCG相结合的NCI-ISG显著提高了MI-EEG分类的准确性和可靠性.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 生物医学工程 生物医学工程
背景情况:
- 运动成像脑电图 (MI-EEG) 是复杂的,表现出非静止性和不均的分布.
- 现有的深度学习方法很难有效地融合和增强多维MI-EEG特征.
- 准确的MI-EEG识别对于先进的脑电脑接口至关重要.
研究的目的:
- 开发一种用于增强MI-EEG数据表示和特征提取的新方法.
- 提高运动图像分类的准确性和可靠性.
- 解决处理复杂MI-EEG特征的现有方法的局限性.
主要方法:
- 开发了一种基于时间频率分析的新道重要性 (NCI) 方法.
- 该NCI方法通过将MI-EEG转换为时间频谱,计算NCI,并创建加权子频段图像来生成图像序列 (NCI-ISG).
- 一个并行的多分支卷积神经网络和门循环单元 (PMBCG) 设计用于空间-光谱和时间特征提取.
主要成果:
- 在两个公开的四类MI-EEG数据集上,NCI-ISG + PMBCG方法实现了98.26%和80.62%的平均准确率.
- 该方法在MI-EEG分类中表现出高于最先进的方法的性能.
- 包括卡帕值,混矩阵和ROC曲线在内的统计评估证实了该方法的有效性.
结论:
- 拟议的NCI-ISG方法有效地增强了跨时频空间域的特征表示.
- NCI-ISG + PMBCG框架显著提高了MI-EEG识别的准确性,可靠性和可辨别性.
- 这项研究为使用MI-EEG信号的脑电脑接口应用提供了有前途的进展.
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