From Electrode to Global Brain: Integrating Multi- and Cross-Scale Brain Connections and Interactions Under Cross-Subject and Within-Subject Scenarios
This work addresses a specific problem in brain-computer interfaces for motor imagery classification, focusing on cross-subject variability and data scarcity, which is incremental as it builds on domain adaptation methods.
The paper tackles the challenge of cross-subject motor imagery classification in data-scarce scenarios by addressing multi-scale spatial data distribution differences, proposing a novel network (MSSDAN) that integrates multi-scale brain topological structures to solve this problem.
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.