TSPNet: a time-spatial parallel network for classification of EEG-based multiclass upper limb motor imagery BCI.

ABSTRACT: The classification of electroencephalogram (EEG) motor imagery signals has emerged as a prominent research focus within the realm of brain-computer interfaces. Nevertheless, the conventional, limited categories (typically just two or four) offered by brain-computer interfaces fail to provide an extensive array of control modes. To address this challenge, we propose the Time-Spatial Parallel Network (TSPNet) for recognizing six distinct categories of upper limb motor imagery. Within TSPNet, temporal and spatial features are extracted separately, with the time dimension feature extractor and spatial dimension feature extractor performing their respective functions. Following this, the Time-Spatial Parallel Feature Extractor is employed to decouple the connection between temporal and spatial

SUBMITTER: Bi J 

PROVIDER: S-EPMC10754979 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature