Single-Trial EEG Classification via Orthogonal Wavelet Decomposition-Based Feature Extraction.
Single-Trial EEG Classification via Orthogonal Wavelet Decomposition-Based Feature Extraction.
复制标题
通过基于正交小波分解的特征提取进行单试验脑电图分类
DOI：
10.3389/fnins.2021.715855
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发表时间：
2021
影响因子：
4.3
通讯作者：
Wu W
中科院分区：
文献类型：
--
作者：
Qi F;Wang W;Xie X;Gu Z;Yu ZL;Wang F;Li Y;Wu W
由于脑电信号的非平稳性和低信噪比（低SNR）特性，实现高分类性能是具有挑战性的。空间滤波通常用于改善SNR，但潜在的时间或频率信息中的个体差异常常被忽略。本文采用正交小波分解的方法对运动想象信号进行研究，将原始信号分解为多个互不相关的子带分量。此外，通过对子带分量进行加权的通道频谱滤波与空间滤波联合实现，以提高EEG信号的可辨别性，其中在目标函数中嵌入l2范数正则化项以解决潜在的过拟合问题。最后，稀疏贝叶斯学习与高斯先验被施加到提取的功率特征，产生RVM分类器。SEOWADE的分类性能明显优于几种竞争算法（CSP，FBCSP，CSSP，CSSSP和浅层ConvNet）。此外，SEOWADE优化的空间滤波器的头皮重量图更具有神经生理学意义。总之，这些结果表明，SEOWADE在提取相关的时空信息的单次试验EEG分类的有效性。
Achieving high classification performance is challenging due to non-stationarity and low signal-to-noise ratio (low SNR) characteristics of EEG signals. Spatial filtering is commonly used to improve the SNR yet the individual differences in the underlying temporal or frequency information is often ignored. This paper investigates motor imagery signals via orthogonal wavelet decomposition, by which the raw signals are decomposed into multiple unrelated sub-band components. Furthermore, channel-wise spectral filtering via weighting the sub-band components are implemented jointly with spatial filtering to improve the discriminability of EEG signals, with an l2-norm regularization term embedded in the objective function to address the underlying over-fitting issue. Finally, sparse Bayesian learning with Gaussian prior is applied to the extracted power features, yielding an RVM classifier. The classification performance of SEOWADE is significantly better than those of several competing algorithms (CSP, FBCSP, CSSP, CSSSP, and shallow ConvNet). Moreover, scalp weight maps of the spatial filters optimized by SEOWADE are more neurophysiologically meaningful. In summary, these results demonstrate the effectiveness of SEOWADE in extracting relevant spatio-temporal information for single-trial EEG classification.
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影响因子：
4.6
作者：
Lemm, S;Blankertz, B;Müller, KR
通讯作者：
Müller, KR
影响因子：
4.8
作者：
Schirrmeister, Robin Tibor;Springenberg, Jost Tobias;Ball, Tonio
通讯作者：
Ball, Tonio
影响因子：
4.4
作者：
Hazarika, N;Chen, JZ;Sergejew, A
通讯作者：
Sergejew, A
影响因子：
4
作者：
Lawhern, Vernon J.;Solon, Amelia J.;Lance, Brent J.
通讯作者：
Lance, Brent J.
RSTFC：单次试验脑电图时空过滤和分类的新算法
DOI：
10.1109/tnnls.2015.2402694
发表时间：
2015-12-01
影响因子：
10.4
作者：
Qi, Feifei;Li, Yuanqing;Wu, Wei
通讯作者：
Wu, Wei