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

According to the study of brain connectomics [29] and the aforementioned statement above, the topological connection Spurred on by the advent of advanced non-invasive techniques of the human brain takes place on three separate levels with such as electroencephalogram (EEG), explorations of different scales, inextricably linked with the geometry of the brain networks have entered a new era [40]. The proposed multi-scale spatial data distribution as a remarkable organ, exhibits a high level of time-varying differences can thus be concluded as three categories under complexity attributed to the intricate nature of the structural different brain scales: connections among its constituent units [4]. To the best of the authors' knowledge, The deep domain adaptation (DDA) method combines the no previous work has integrated the multi-scale spatial data superiority of deep learning and transfer learning, becoming distribution problem with the deep domain adaptation network one of the most efficient tools to address the data distribution (DDAN), neither on the design of the CNN structure nor difference problem in cross-subject EEG classification tasks the establishment of the adaptation domain. More and more researchers utilize this powerful integrate the principles of multi-scale brain topological structures tool to solve cross-subject motor imagery (MI) classification in order to solve the multi-scale spatial data distribution problems [35], [37], [38], aiming to improve the model generalization difference problem [29], a novel multi-scale spatial domain and the classification performance by transferring adaptation network (MSSDAN) consists of both multi-scale knowledge from source domain subject. The existing three types of crosssubject A. Overview of MSSDAN MI classification (MTM: multi-source to multi-target, MTS: multi-source to single-target, and STS) DDA methods In this paper, we propose MSSDAN, a new domain adaptation focus more on the global [15], [39], [41], class [14], [20], and method for the brain-computer interface, which consists of temporal domain adaptations [2], [5].