一个深度学习模型用于电脑图信号和语音刺激之间的相关性分析
Michele Alessandrini1, Laura Falaschetti1, Giorgio Biagetti1
1Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, I-60131 Ancona, Italy.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
这项研究引入了一种新的神经网络框架,通过最大限度地与语音刺激的相关性来改善电脑电图 (EEG) 分析. 这种新方法增强了大脑反应检测,超过了现有的深度学习方法.
科学领域:
- 神经科学是一个神经科学.
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 脑电图 (EEG) 是一种用于大脑监测的非侵入性工具.
- 脑电图记录中的人工物阻碍了精确的刺激-反应分析.
- 目前的方法经常使用线性分析,如正规相关性分析 (CCA),以减轻文物.
研究的目的:
- 引入一种新的相关性分析 (CA) 框架,使用EEG和语音刺激的神经网络.
- 为了提高从EEG信号中提取功能性大脑反应的准确性.
- 改进现有的深度学习CA (DCCA) 方法.
主要方法:
- 开发了一个新的CA框架,利用单层多层感知器 (MLP) 神经网络.
- 设计了一个特定的损失函数,以最大限度地提高EEG信号和语音刺激之间的相关性.
- 将拟议的方法与线性CCA (LCCA) 和DCCA进行比较,使用来自听话的受试者的EEG数据.
主要成果:
- 拟议的神经网络框架证明了相关性分析的改进.
- 与最先进的DCCA方法相比,Pearson整体相关性增加了10.56%.
- 单一的MLP网络方法在增强刺激-反应相关性方面被证明是有效的.
结论:
- 基于神经网络的新型CA框架为EEG语音刺激分析提供了显著的改进.
- 这种方法有效地减轻了文物,并增强了对大脑反应的检测.
- 该方法比现有的线性和深度学习技术具有有前途的进步.
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