一个在主体内的多模式NIRS-EEG分类器用于婴儿数据
Jessica Gemignani1,2, Judit Gervain1,2,3
1Department of Developmental and Social Psychology, University of Padua, Via Venezia, 8, 35131 Padua, Italy.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
结合功能近红外光谱 (fNIRS) 和电脑图像 (EEG) 神经成像可以增强婴儿语音处理分析. 这种新的框架提高了个体婴儿大脑活动的分类准确性,为发育神经科学提供了更深入的见解.
科学领域:
- 发育神经科学的发展神经科学.
- 神经成像是一种神经成像.
- 认知神经科学 认知神经科学
背景情况:
- 功能近红外光谱 (fNIRS) 和电脑图 (EEG) 是发育神经科学中关键的神经成像工具.
- 同时的fNIRS-EEG记录提供了补充数据,但提出了分析挑战,限制了婴儿研究.
- 现有的方法往往侧重于群体层面的分析,阻碍了婴儿个体层面的洞察力.
研究的目的:
- 提出和验证婴儿fNIRS-EEG数据组合的多变量模式分析框架.
- 为了使敏感的,个体级分类大脑对听觉刺激的反应.
- 评估fNIRS-EEG综合分析与单模方法相比的性能.
主要方法:
- 开发了一个使用fNIRS-EEG特征矩阵组合的多变量模式分析框架.
- 在更大的fNIRS块中选择了EEG试验,并结合了相应的特征.
- 在5名新生儿的fNIRS-EEG数据上测试了分类器,听取人类言语和子发声.
主要成果:
- 仅使用fNIRS数据,分类器在五分之三的婴儿中实现了高,统计学上显著的准确性.
- 结合的fNIRS-EEG数据产生了更高的分类准确性,特别是使用血红蛋白的两个组成部分.
- 对于两个总体准确度较低的婴儿,其中一个婴儿在fNIRS-EEG数据组合后表现出最佳表现.
结论:
- 一个多变量模式分析框架有效地整合了fNIRS和EEG数据用于婴儿神经成像.
- 结合fNIRS-EEG分析显著提高了个体婴儿大脑活动的分类准确性.
- 这种方法为详细调查婴儿听觉处理和发育神经科学研究提供了一个有希望的工具.
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