From Electrode to Global Brain: Integrating Multi- and Cross-Scale Brain Connections and Interactions Under Cross-Subject and Within-Subject Scenarios

• URL: http://arxiv.org/abs/2411.05862v1
• Date: Thu, 07 Nov 2024 09:12:13 GMT
• Title: From Electrode to Global Brain: Integrating Multi- and Cross-Scale Brain Connections and Interactions Under Cross-Subject and Within-Subject Scenarios
• Authors: Chen Zhige, Qin Chengxuan,
• Abstract summary: The individual variabilities of electroencephalogram signals pose great challenges to cross-subject motor imagery (MI) classification. 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. 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.
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• Abstract: 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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