Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM
The study highlights the effectiveness of combining advanced signal processing and machine learning techniques to improve the robustness and generalization of EEG-based attention classification. Limitations include the small sample size and limited demographic diversity, suggesting future research should expand participant groups and explore broader applications, such as mental health monitoring and neurofeedback.
Authors
- Yehang Chen (ORCID: https://orcid.org/0009-0002-3724-3957)
- Yun‐De Li
- Chengliang Jiang
- Zijun Huang (ORCID: https://orcid.org/0000-0002-6038-4320)
- Jing He (ORCID: https://orcid.org/0000-0002-4819-0136)
- Feng Jin (ORCID: https://orcid.org/0000-0002-0325-5362)
- Jiangfeng Shi
Institutions
- Guilin University of Aerospace Technology (CN)
- Guilin University of Electronic Technology (CN)
- South China University of Technology (CN)
Publication Details
- Journal
- Frontiers in Human Neuroscience
- Published
- 2025-01-09
- DOI
- https://doi.org/10.3389/fnhum.2024.1481493
- Citations
- 3
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 4.29