A Task-Related EEG Microstate Clustering Algorithm Based on Spatial Patterns, Riemannian Distance, and a Deep Autoencoder
The proposed unsupervised algorithm can autonomously discretize EEG signals into a finite number of microstates, thereby facilitating investigations into the temporal structures underlying cognitive processes.
Authors
- T. Shen (ORCID: https://orcid.org/0009-0007-0287-7171)
- Lichen Shi (ORCID: https://orcid.org/0000-0001-5408-9205)
- Yongxiang Lian
- Shihao Pan (ORCID: https://orcid.org/0009-0005-7808-4958)
Institutions
- Beihang University (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Brain Sciences
- Published
- 2024-12-29
- DOI
- https://doi.org/10.3390/brainsci15010027
- Citations
- 5
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.71
Funders
- National Social Science Fund of China