Motor imagery EEG classification via wavelet-packet synthetic augmentation and entropy-based channel selection

Integrating WPD-based augmentation with WPEE-driven channel selection yields higher MI decoding accuracy with fewer channels and without extra recordings. The framework offers a computationally efficient, clinically viable paradigm for enhanced EEG classification in resource-constrained settings.

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Publication Details

Journal
Frontiers in Neuroscience
Published
2025-11-10
DOI
https://doi.org/10.3389/fnins.2025.1689647
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
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article

Motor imagery EEG classification via wavelet-packet synthetic augmentation and entropy-based channel selection

Z. Qian, Tong Zhao, Minmin Zheng
Frontiers in Neuroscience
EEG and Brain-Computer Interfaces
article

Motor imagery EEG classification via wavelet-packet synthetic augmentation and entropy-based channel selection

Z. Qian, Tong Zhao, Minmin Zheng
article en

Abstract

Integrating WPD-based augmentation with WPEE-driven channel selection yields higher MI decoding accuracy with fewer channels and without extra recordings. The framework offers a computationally efficient, clinically viable paradigm for enhanced EEG classification in resource-constrained settings.

Frontiers in NeuroscienceVol. 19
Putian University (CN)
Openalex Percentile: Top 66%
EEG and Brain-Computer Interfaces
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Motor imagery EEG classification via wavelet-packet synthetic augmentation and entropy-based channel selection — Z. Qian, Tong Zhao, et al. · Frontiers in Neuroscience (2025) | TGRS Research Map | TGRS