工作内存负载识别与深度学习时间序列分类分类
Richong Pang1,2, Haojun Sang3, Li Yi4
1Barco Technology Limited, Zhuhai 519031, China.
Biomedical optics express
|June 10, 2024
概括
本研究介绍了一种深度学习模型,用于使用fNIRS大脑信号来解码工作记忆负载 (WML). 新的TAResnet-BiLSTM模型在对象间WML检测中实现了92.4%的准确性.
科学领域:
- 神经科学是一个神经科学.
- 人与计算机的交互
- 机器学习 机器学习
背景情况:
- 工作记忆负载 (WML) 是人机交互中的一个关键信号.
- 准确的WML评估对于有效的应用程序至关重要.
- 现有的方法在主体间解码方面面临挑战.
研究的目的:
- 提出一个深度学习 (DL) 时间序列分类 (TSC) 模型,用于跨学科的WML解码.
- 使用功能近红外光谱学 (fNIRS) 数据来评估模型的性能.
- 推进脑计算机接口 (BCI) 应用程序的实时WML检测.
主要方法:
- 在视觉工作记忆任务中收集了27名参与者的fNIRS血液动力学信号.
- 采用传统的机器学习 (LDA,SVM) 进行主体内部解码.
- 开发并应用了一种新的深度学习模型,TAResnet-BiLSTM,用于跨主题解码.
主要成果:
- 在主体内分类的准确性达到94.6% (LDA) 和79.1% (SVM).
- 拟议的TAResnet-BiLSTM模型实现了92.4%的高跨主体WML解码精度.
- 在交叉参与者WML解码中证明DL模型的优越性能.
结论:
- TAResnet-BiLSTM模型为跨主体WML解码提供了一个有希望的方法.
- fNIRS与DL相结合,为BCI中实时WML检测提供了一种可行的方法.
- 这项研究为认知负载评估中的脑计算机接口开辟了新的途径.
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