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处理技术对大脑与计算机接口系统分类准确性的影响

András Adolf^1,2,3, Csaba Márton Köllőd^2,3, Gergely Márton^2,3

¹Roska Tamás Doctoral School of Sciences and Technology, Práter utca 50/a, 1083 Budapest, Hungary.

|January 8, 2025

概括

为大脑计算机接口 (BCI) 优化脑电图 (EEG) 信号分类,需要仔细考虑处理步骤. 转移学习显著提高了准确性,但文物拒绝和频率过效应因网络和主题而异.

科学领域:

神经科学和机器学习
大脑计算机接口 (BCI) 信号处理信号处理
计算神经科学是一种神经科学.

背景情况:

精确的脑电图 (EEG) 信号分类对于脑电脑接口 (BCI) 系统至关重要,特别是在神经康复中.
处理管道的选择显著影响BCI分类性能.
本研究研究了各种预处理步骤对EEG分类准确性的影响.

研究的目的:

系统地评估不同处理技术对基于EEG的BCI分类准确性的影响.
评估文物拒绝,频率过,转移学习和裁剪训练的有效性.
分析各种卷积神经网络架构 (EEGNet,浅层 ConvNet,多分支 Conv3D Net,Conv2D Net,Conv3D Net) 的性能.

主要方法:

使用了Physionet数据集与四个运动图像类.
用包括FASTER算法在内的技术处理的原始和文物拒绝的EEG数据进行比较.
评估了频率过,转移学习和在不同的网络架构 (包括3D卷积网络) 中裁剪培训策略.

主要成果:

文物拒绝对分类准确性的影响是主体和网络依赖的.
转移学习持续改善了网络性能,特别是在未过的数据上 (例如,准确率为46.1%至63.5%).
较低频率组件通常产生更好的分类;较高频率更具歧视性,用于特定网络的培训.

结论:

处理步骤和神经网络性能之间的相互作用是复杂的.
定制的处理策略对于优化个人主体和特定网络架构的BCI性能至关重要.
对于强大的BCI应用,对量身定制的预处理管道进行进一步的研究是有必要的.

关键词:

在美国,CNN是CNN.艺术品的拒绝艺术品的拒绝大脑-计算机接口接口电脑脑电图 (EEG) 是一种电脑电图.速度更快,更快,更快.运动图像图像学

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