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基于消除联合混合递归特征的通道选择和ResGCN用于跨会话MI识别

Duan Li¹, Keyun Li¹, Yongquan Xia²

¹School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, Henan, China.

Scientific reports

|October 9, 2024

概括

这项研究引入了一种新的混合递归特征消除 (H-RFE) 方法,用于在脑电脑接口 (BCI) 中选择脑电图 (EEG) 通道. 该方法通过优化BCI系统的通道选择,显著提高了运动图像 (MI) 识别精度.

科学领域:

神经科学和生物医学工程
信号处理和机器学习

背景情况:

使用多通道脑电图 (EEG) 的脑电脑接口 (BCI) 对运动图像 (MI) 识别在诸如感官补偿等应用中至关重要.
个人变异性和噪音高的EEG数据对BCI性能构成挑战,需要有效的道选择策略.

研究的目的:

开发和评估一种新的通道选择方法,即混合递归特征消除 (H-RFE),用于改进BCI系统中的运动图像识别.
将H-RFE与余图神经网络集成,以有效利用时空EEG信息.

主要方法:

采用混合递归特征消除 (H-RFE) 策略进行适应性通道选择,使用随机森林,梯度增强和后勤回归作为评估器.
用剩余块的图形神经网络来处理多通道EEG数据并识别运动图像任务.
提出的方法在SHU和PhysioNet数据集上得到了验证.

主要成果:

H-RFE方法实现了高跨会话MI识别准确性:在使用73.44%的道的SHU数据集上达到90.03%,在使用72.5%的道的PhysioNet数据集上达到93.99%.
与传统方法相比,拟议的道选择方法在分类准确度方面取得了显著的改进,包括在基于相关性和基于相互信息的选择方面取得了显著的收益.
该方法有效地减少了通道冗余和噪声,从而提高了BCI性能.

结论:

拟议的H-RFE与残余图神经网络相结合,为基于运动图像的BCI中的通道选择提供了一种优越的方法.
这种方法通过适应性地选择适合个体受试者的最佳EEG通道来提高BCI的准确性和效率.
这些发现表明,开发更强大,更精确的大脑与计算机接口系统是一个有前途的方向.

关键词:

大脑与计算机接口 (BCI)频道选择频道选择深度学习是一种深度学习.图形卷积神经网络 (GCN) 的图形.运动图像 (MI)

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