A Local-Ascending-Global Learning Strategy for Brain-Computer Interface

Authors

  • Dongrui Gao Chengdu University of Information Technology University of Electronic Science and Technology of China
  • Haokai Zhang Chengdu University of Information Technology
  • Pengrui Li Chengdu University of Information Technology University of Electronic Science and Technology of China
  • Tian Tang Chengdu University of Information Technology
  • Shihong Liu Chengdu University of Information Technology
  • Zhihong Zhou Chengdu University of Information Technology
  • Shaofei Ying University of Electronic Science and Technology of China
  • Ye Zhu Chengdu University of Information Technology
  • Yongqing Zhang Chengdu University of Information Technology

DOI:

https://doi.org/10.1609/aaai.v38i9.28867

Keywords:

HAI: Human-Computer Interaction, HAI: Applications

Abstract

Neuroscience research indicates that the interaction among different functional regions of the brain plays a crucial role in driving various cognitive tasks. Existing studies have primarily focused on constructing either local or global functional connectivity maps within the brain, often lacking an adaptive approach to fuse functional brain regions and explore latent relationships between localization during different cognitive tasks. This paper introduces a novel approach called the Local-Ascending-Global Learning Strategy (LAG) to uncover higher-level latent topological patterns among functional brain regions. The strategy initiates from the local connectivity of individual brain functional regions and develops a K-Level Self-Adaptive Ascending Network (SALK) to dynamically capture strong connectivity patterns among brain regions during different cognitive tasks. Through the step-by-step fusion of brain regions, this approach captures higher-level latent patterns, shedding light on the progressively adaptive fusion of various brain functional regions under different cognitive tasks. Notably, this study represents the first exploration of higher-level latent patterns through progressively adaptive fusion of diverse brain functional regions under different cognitive tasks. The proposed LAG strategy is validated using datasets related to fatigue (SEED-VIG), emotion (SEED-IV), and motor imagery (BCI_C_IV_2a). The results demonstrate the generalizability of LAG, achieving satisfactory outcomes in independent-subject experiments across all three datasets. This suggests that LAG effectively characterizes higher-level latent patterns associated with different cognitive tasks, presenting a novel approach to understanding brain patterns in varying cognitive contexts.

Published

2024-03-24

How to Cite

Gao, D., Zhang, H., Li, P., Tang, T., Liu, S., Zhou, Z., Ying, S., Zhu, Y., & Zhang, Y. (2024). A Local-Ascending-Global Learning Strategy for Brain-Computer Interface. Proceedings of the AAAI Conference on Artificial Intelligence, 38(9), 10039-10047. https://doi.org/10.1609/aaai.v38i9.28867

Issue

Section

AAAI Technical Track on Humans and AI