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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
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多域卷积神经网络模型用于改进与事件相关的潜在分类.

Xiaoqian Chen1, Resh S Gupta2, Lalit Gupta1

  • 1School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University, Carbondale, IL 62901, USA.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括
此摘要是机器生成的。

新的卷积神经网络 (CNN) 模型通过融合多域信息,准确地分类事件相关潜力 (ERP). 这些模型为大脑-计算机接口和大脑疾病分类提供了高精度.

关键词:
连续波形变换连续波形变换.卷积神经网络是一个卷积神经网络.与事件相关的潜力.多域分类器 多域分类器扫描图,扫描图,扫描图,扫描图

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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:

  • 与事件相关的潜能 (ERP) 是理解大脑活动的关键神经信号.
  • 对ERP的准确分类对于诸如脑计算机接口 (BCI) 和诊断大脑疾病等应用至关重要.
  • 现有的方法往往难以应对ERP数据在不同受试者和试验中的复杂性和可变性.

研究的目的:

  • 为准确的ERP分类引入两个新的卷积神经网络 (CNN) 模型.
  • 从ERP的连续波波变换 (CWT) 中融合频率,时间和空间域信息.
  • 评估模型在个人 (BCI) 和基于组的 (疾病分类) 场景中的性能.

主要方法:

  • 开发了两种多域CNN模型,利用从CWT衍生的Z-scalograms和V-scalograms.
  • 将多通道ERP数据合并为频率-时间-空间立方体 (第一模型) 和矩阵 (第二模型).
  • 为定制 (单个主题) 和基于组 (跨主题) 的ERP分类进行了实验.

主要成果:

  • 两种多领域模型都实现了单个试验的高分类准确度,并平均了ERP.
  • 即使在一小部分排名最高的频道中,也证明了有效的分类.
  • 建议的多域融合模型始终优于单通道分类器.

结论:

  • 新的多域CNN模型有效地利用ERP分类的融合频率,时间和空间信息.
  • 这些模型对BCI应用和大脑疾病分类都有显著的前景.
  • 融合方法为ERP分析提供了一个优越的替代方案,而不是传统的单通道方法.