The electroencephalogram (EEG) signal is one of the most frequently used biomedical signals. In order to accurately exploit the cosparsity and low-rank property which is nature in multichannel EEG signals, motivated by the fact that weighted schatten-p norm and \({l_q}\) norm can better approximate the matrix rank and \({l_0}\) norm, in this paper, a non-convex optimization model is proposed to precisely reconstruct the multichannel EEG signal. weighted schatten-p norm and \({l_q}\) norm are used to enforce low-rank property and cosparsity. In addition, an efficient iterative optimization method based on alternating direction method of multipliers is used to solve the resulting non-convex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform existing state-of-the-art CS methods for compressive sensing of multichannel EEG signals.

脑电图（EEG）信号是最常用的生物医学信号之一。为了准确利用多通道EEG信号中的自然稀疏性和低秩特性，这是受加权schatten- p范数和\（{l_q} \）范数可以更好地逼近矩阵秩和\（{l_0 } \）范数，本文提出了一种非凸优化模型来精确重构多通道脑电信号。加权schatten- p范数和\（{l_q} \）规范用于强制执行低级属性和稀疏性。另外，基于乘法器交替方向法的有效迭代优化方法被用来解决由此产生的非凸优化问题。实验结果表明，该算法可以显着优于现有的多通道EEG信号压缩感知的CS方法。