Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM

Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM

IntroductionAttention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method com...

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Bibliographic Details
Main Authors: Jing He, Zijun Huang, Yunde Li, Jiangfeng Shi, Yehang Chen, Chengliang Jiang, Jin Feng
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Human Neuroscience
Subjects:
brain-computer interfaces
attentional state
robust Kalman
convex optimization
norm-ELM
Online Access:https://www.frontiersin.org/articles/10.3389/fnhum.2024.1481493/full
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