功能连接和功能融合增强多类电机-图像脑电脑接口性能
Ilaria Siviero1, Gloria Menegaz2, Silvia Francesca Storti2
1Department of Computer Science, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
这项研究引入了通过融合翻译不变特征 (TIF) 和大脑连接特征 (BCF) 的运动图像脑计算机接口 (MI-BCI) 的新框架. 这种方法通过克服单通道分析的局限性,显著提高MI-BCI性能.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 机器图像大脑计算机接口 (MI-BCI) 在提取多个任务的区分特征方面面临着挑战.
- 使用单个脑电图 (EEG) 通道的传统方法忽略了相互连接,限制了结果.
- 功能性大脑连接 (FC) 是有前途的,但受到高主体变异性的阻碍.
研究的目的:
- 开发一种新的信号处理框架,以增强多类MI-BCI的特征提取.
- 解决单通道功能的局限性和功能连接的可变性问题.
- 为了提高MI-BCI系统的性能和准确性.
主要方法:
- 使用散射卷积网络 (SCN) 提取的转换不变特征 (TIF).
- 提取的大脑连接特征 (BCFs) 来捕捉通道间的关系.
- 采用了功能融合策略,将TIF和BCF从选定的道中结合起来.
- 使用多类支向量机 (SVM) 进行分类.
主要成果:
- 拟议的特征融合框架在公共数据集 (BCI竞争IV,数据集IIa) 上优于现有的最先进方法.
- 与BCF合并的TIF与单独使用TIF相比,产生了更好的结果.
- 证明了结合特征方法在提高MI-BCI性能方面的有效性.
结论:
- 开发的框架为改善多类MI-BCI系统性能提供了一个有希望的解决方案.
- TIF和BCF的功能融合是克服以前限制的关键策略.
- 这种方法有可能推动脑-计算机接口领域的发展.
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