A study of motor imagery EEG classification based on feature fusion and attentional mechanisms

The model demonstrates robustness to spatial resolution variations and individual differences, effectively mitigating key challenges in motor imagery EEG classification.

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Institutions

Publication Details

Journal
Frontiers in Human Neuroscience
Published
2025-07-16
DOI
https://doi.org/10.3389/fnhum.2025.1611229
Citations
3
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
4.29

Funders

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article

A study of motor imagery EEG classification based on feature fusion and attentional mechanisms

Hailin Tang, Zhijian Wu, Shijun Li, Yijia Li et al.
3 citations
Frontiers in Human Neuroscience
EEG and Brain-Computer Interfaces
4.29
article

A study of motor imagery EEG classification based on feature fusion and attentional mechanisms

Hailin Tang, Zhijian Wu, Shijun Li, Yijia Li, Lei Jiang, Zhu Tingting
article en
3 citations

Abstract

The model demonstrates robustness to spatial resolution variations and individual differences, effectively mitigating key challenges in motor imagery EEG classification.

Frontiers in Human NeuroscienceVol. 19
Wuhan University (CN), RPX (United States) (US), Guangdong Baiyun University (CN)
Wuhan University
Openalex Percentile: Top 6%
EEG and Brain-Computer Interfaces
4.29
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