尺度自适应的多小波脑电稀疏时变建模与时频表征方法
Sparse Time-Varying Modeling and Time-Frequency Representation of EEG Data Using Scale-Adaptive Multiwavelet
- 电子学报 2025年53卷第8期 页码：2707-2718
- 作者机构：
1.福州大学电气工程与自动化学院，福建福州 350108
2.福建省医疗器械和医药技术重点实验室，福建福州 350108
- 作者简介：
[ "郑楠 男，1996年11月出生于广东省饶平县.现为福州大学电气工程与自动化学院博士研究生.主要研究方向为生物医学信号的建模与分析.E-mail: zn190120078@163.com" ]
[ "李玉榕 女，1973年2月出生于福建省莆田市.现为福州大学电气工程与自动化学院教授、博士生导师.主要研究方向为多模态电生理信号建模与智能康复技术.E-mail: liyurong@fzu.edu.cn" ]
- 基金信息：国家自然科学基金(62373108)
DOI：10.12263/DZXB.20250180
中图分类号： TP391;收稿：2025-03-11，
录用：2025-08-01，
纸质出版：2025-08-25
- 稿件说明：
移动端阅览
郑楠, 李玉榕, 史武翔, 等. 尺度自适应的多小波脑电稀疏时变建模与时频表征方法[J]. 电子学报, 2025, 53(08): 2707-2718.
ZHENG Nan, LI Yu-rong, SHI Wu-xiang, et al. Sparse Time-Varying Modeling and Time-Frequency Representation of EEG Data Using Scale-Adaptive Multiwavelet[J]. Acta Electronica Sinica, 2025, 53(08): 2707-2718.
郑楠, 李玉榕, 史武翔, 等. 尺度自适应的多小波脑电稀疏时变建模与时频表征方法[J]. 电子学报, 2025, 53(08): 2707-2718. DOI：10.12263/DZXB.20250180
ZHENG Nan, LI Yu-rong, SHI Wu-xiang, et al. Sparse Time-Varying Modeling and Time-Frequency Representation of EEG Data Using Scale-Adaptive Multiwavelet[J]. Acta Electronica Sinica, 2025, 53(08): 2707-2718. DOI：10.12263/DZXB.20250180
摘要
时频表征的准确性直接影响脑电信号内在含义和动态特性的解析.针对基于多小波的时频表征方法中存在的尺度固定、回归项选择不当等问题，本文提出一种基于尺度自适应稀疏多小波的时频表征框架，以提升表征精度.该方法通过稀疏贝叶斯学习-信息熵联合优化，从全局角度筛选时变模型的最优回归项，有效规避传统方法的局部收敛缺陷；进一步，为小波基分配尺度，从最优个体、粒子群变异和种群更新3个方面改进遗传算法并进行尺度寻优，实现小波基与最优尺度的自适应匹配，增强多小波基对时变信号的拟合能力.最终，估计的时变参数经由参数谱估计转化为准确的时频表征.在3个仿真模型的实验结果表明，所提方法至少降低23.08%的时变参数估计误差，增强2.93%的时频信息估计精度，在动态参数跟踪和时频信息估计上展现出强大竞争力.在BCI Competition II-data set III的实验结果显示，所提方法在估计事件相关同步/去同步的性能较先进时变建模方法增强（3.37→8.78）；进一步将所提方法提取的时频信息与简单卷积神经网络结合，即可在BCI Competition IV-data set 2b中取得与最先进但复杂的分类模型相当的识别准确率（88.04%），侧面证实了所提方法的时频表征能力.本文方法从模型结构筛选、寻优算法改进和基函数尺度配置3个方面进行设计，实现时变参数估计准确率与时频分辨率的协同提升，为脑电信号处理提供了一种新方法.
Abstract
The accuracy of time-frequency representation directly influences the interpretation of the intrinsic dynamics and functional significance of electroencephalogram (EEG) signals. To address the limitations of fixed scales and suboptimal regression term selection in existing multi-wavelet-based methods
this paper proposes a novel time-frequency representation framework based on scale-adaptive sparse multi-wavelets. This method adopts a joint sparse Bayesian learning and information entropy optimization framework to globally identify the optimal regression terms of the time-varying model
effectively avoiding the local convergence issues of traditional approaches. Furthermore
scales are allocated to the wavelet basis. The genetic algorithm is enhanced in three key aspects—optimal individual selection
particle swarm mutation
and population update—to optimize the scale. This achieves adaptive matching between the wavelet basis and the optimal scale
thus enhancing the fitting capability of multiple wavelet bases for time-varying signals. Ultimately
the estimated time-varying parameters are transformed into accurate time-frequency representations through parameter spectral estimation. Experiments on three simulation models show at least a 23.08% reduction in parameter estimation error and a 2.93% improvement in time-frequency resolution. Compared to state-of-the-art algorithms
it shows strong competitiveness in tracking time-varying parameters and extracting time-frequency features. On BCI Competition II-data set III
our method enhances event-related desynchronization/event-related synchronization detection
with performance improving from 3.37 to 8.78. When combined with a simple convolutional neural network
it achieves 88.04% recognition accuracy on the BCI Competition IV-dataset 2b—comparable to that of more complex state-of-the-art models—thereby indirectly validating its effectiveness in time-frequency representation. Our method is designed from three perspectives: model structure optimization
algorithm enhancement
and basis function scaling. The collaborative improvement of time-varying parameter estimation and time-frequency resolution is successfully achieved
offering a novel methodology for EEG signal.
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Keywords
references
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