解决脑电跨被试运动想象分类中的多尺度空间差异问题
From Electrode to Global Brain: Integrating Multi- and Cross-Scale Brain Connections and Interactions Under Cross-Subject and Within-Subject Scenarios
- 设计多尺度空间特征提取与域适应联合网络
- 在单源单目标场景下显著提升跨被试分类准确率
- 适合脑机接口中个体差异大、数据少的场景
个体脑电信号差异给跨被试运动想象（MI）分类带来巨大挑战，尤其在数据稀缺的单源单目标（STS）场景下。由于脑结构拓扑和连接关系是人脑固有属性，多尺度空间数据分布差异无法完全消除。现有研究尚未探讨此多尺度空间分布问题在跨被试与同被试场景下的影响。本文提出一种新型多尺度空间域适应网络（MSSDAN），包含多尺度空间特征提取器（MSSFE）与多尺度空间域适应方法（MSSDA），旨在整合多尺度脑拓扑结构原理，解决多尺度空间数据分布差异问题。
原文摘要 · Abstract (English)
The individual variabilities of electroencephalogram signals pose great challenges to cross-subject motor imagery (MI) classification, especially for the data-scarce single-source to single-target (STS) scenario. The multi-scale spatial data distribution differences can not be fully eliminated in MI experiments for the topological structure and connection are the inherent properties of the human brain. Overall, no literature investigates the multi-scale spatial data distribution problem in STS cross-subject MI classification task, neither intra-subject nor inter-subject scenarios. In this paper, a novel multi-scale spatial domain adaptation network (MSSDAN) consists of both multi-scale spatial feature extractor (MSSFE) and deep domain adaptation method called multi-scale spatial domain adaptation (MSSDA) is proposed and verified, our goal is to integrate the principles of multi-scale brain topological structures in order to solve the multi-scale spatial data distribution difference problem.
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