电子学报2025，Vol.53Issue(8)：2707-2718,12.DOI:10.12263/DZXB.20250180
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
摘要
Abstract
The accuracy of time-frequency representation directly influences the interpretation of the intrinsic dynam-ics and functional significance of electroencephalogram(EEG)signals.To address the limitations of fixed scales and subop-timal regression term selection in existing multi-wavelet-based methods,this paper proposes a novel time-frequency repre-sentation 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 mod-el,effectively avoiding the local convergence issues of traditional approaches.Furthermore,scales are allocated to the wave-let 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 estima-tion.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 en-hances 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 optimiza-tion,algorithm enhancement,and basis function scaling.The collaborative improvement of time-varying parameter estima-tion and time-frequency resolution is successfully achieved,offering a novel methodology for EEG signal.关键词
运动想象脑电信号/稀疏建模/时变建模/时频分析/多尺度小波Key words
motor imagery electroencephalography/sparse modeling/time-varying modeling/time-frequency analy-sis/multi-scale wavelet分类
信息技术与安全科学引用本文复制引用
郑楠,李玉榕,史武翔,谭济宇,陈文升..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(8):2707-2718,12.基金项目
国家自然科学基金(No.62373108) National Natural Science Foundation of China(No.62373108) （No.62373108）
