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时间注意力融合网络具有自定义损失功能,用于EEG-fNIRS分类

Chayut Bunterngchit^1,2, Jiaxing Wang¹, Jianqiang Su^1,2

¹State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.

Journal of neural engineering

|November 4, 2024

概括

一个新的时间注意力融合网络 (TAFN) 使用脑电图 (EEG) 和功能近红外光谱 (fNIRS) 准确检测大脑活动. 这种先进的方法在认知任务中达到99%以上的准确性,在运动图像中达到97%,有助于检测神经系统疾病.

科学领域:

神经科学和生物医学工程
大脑与计算机的接口
神经系统疾病诊断诊断神经系统疾病诊断

背景情况:

准确检测大脑活动对于理解和管理神经系统疾病至关重要.
结合脑电图 (EEG) 和功能近红外光谱 (fNIRS) 提供了相对于单个模式的协同优势.
现有的多式联动EEG-fNIRS方法面临着阶级不平衡和学科间变异性方面的挑战.

研究的目的:

开发一种新的时间注意力融合网络 (TAFN),用于增强多式模式大脑活动分析.
使用自定义损失函数来解决EEG-fNIRS数据中的类不平衡和类间变异.
提高检测认知和运动意图以及微妙的神经模式的准确性.

主要方法:

提出了一个时间注意力融合网络 (TAFN),将注意力机制与LSTM和时间卷积层集成在一起.
开发了一个自定义的损失函数,包含类权重和不对称的术语,以处理数据不平衡.
使用EEG-fNIRS数据对认知和运动图像任务的跨学科分类进行TAFN性能评估.

主要成果:

TAFN实现了异常的跨主题准确性: >99%的认知任务和>97%的运动图像 (MI).
该模型在识别与相关的微妙差异方面表现出有效性.
在MI任务中的头皮拓分析提供了关于发病检测能力的见解.

结论:

在高精度的大脑活动检测方面,TAFN模型显著优于传统方法.
这种技术对需要识别微妙的神经模式差异的应用有希望,例如和发作检测.
开发的TAFN为推进神经疾病的理解和诊断提供了一个强大的工具.

关键词:

这是一个EEGEEGEEGEEGEEGEEGEEG.大脑 - 计算机接口定制损失的自定义损失在FNIRS中使用.时间的注意力时间的注意力

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