MI-CAT:一个基于变压器的域适应网络,用于机动图像分类
Dongxue Zhang1, Huiying Li1, Jingmeng Xie2
1Jilin University, College of Computer Science and Technology, Changchun, Jilin Province, China; Key Laboratory of Symbol Computation and Knowledge Engineering, Jilin University, Changchun 130012, China.
概括
这项研究介绍了MI-CAT,一种基于变压器的新型架构,用于运动成像 (MI) 脑计算机接口 (BCI). MI-CAT有效地在受试者之间传输知识,大大提高了EEG信号解码的准确性.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 生物医学工程 生物医学工程
背景情况:
- 脑电图 (EEG) 对于运动成像 (MI) 脑电脑接口 (BCI) 是非常重要的,因为它的安全性和方便性.
- 深度学习,特别是变压器模型,显示出EEG信号解码的希望,因为它们能够专注于全球信息.
- 脑电图信号的个体对个体的变异性对BCI的跨领域概括提出了重大挑战.
研究的目的:
- 为了应对基于变压器的BCI中特定对象的EEG信号变化的挑战.
- 提出一种新的架构,MI-CAT,用于MI-BCI中有效的跨领域知识传输.
- 通过利用来自多个受试者的数据来提高单个受试者EEG解码的分类性能.
主要方法:
- 开发了MI-CAT,这是一种利用变压器的自我注意力和交叉注意力机制的新型架构.
- 采用了补丁嵌入层来分割源和目标EEG特征到补丁中.
- 集成多个交叉变压器块 (CTB) 进行自适应的双向知识传输和特定领域的注意力块进行特征对齐.
主要成果:
- 在公共数据集上实现了竞争性分类准确性:在数据集IIb上达到85.26%,在数据集IIa上达到76.81%.
- 使用交叉注意力,在源域和目标域之间展示了有效的功能交互和对齐.
- 验证了模型捕获域依赖信息的能力,以改进EEG解码.
结论:
- 在运动图像BCI中,MI-CAT提供了一种强大的解码EEG信号的解决方案.
- 拟议的架构有效地减轻了特定主体EEG信号差异的影响.
- 这项工作促进了变压器应用在脑电脑界面开发中的进步.
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