Learning EEG Motor Characteristics via Temporal-Spatial Representations

• Published in: IEEE transactions on emerging topics in computational intelligence Vol. 9; no. 1; pp. 933 - 945
• Main Authors: Xiang, Tian-Yu, Zhou, Xiao-Hu, Xie, Xiao-Liang, Liu, Shi-Qi, Yang, Hong-Jun, Feng, Zhen-Qiu, Gui, Mei-Jiang, Li, Hao, Huang, De-Xing, Liu, Xiu-Ling, Hou, Zeng-Guang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Brain modeling; CNN-GNN structure; Electrodes; Electroencephalogram; Electroencephalography; Feature extraction; Graph neural networks; Graphical representations; Human-computer interface; Imaging techniques; Learning; motor characteristics; Motors; Multisensor fusion; Neural networks; Spatial data; Spatiotemporal data; Task analysis; temporal-spatial representations; Time-frequency analysis

Electroencephalogram (EEG) is a widely used neural imaging technique for modeling motor characteristics. However, current studies have primarily focused on temporal representations of EEG, with less emphasis on the spatial and functional connections among electrodes. This study introduces a novel tw...