School of Electrical and Information Engineering, Tianjin University, Tianjin, People's Republic of China.
Department of Physics, Paderborn University, Warburger Straße 100, 33098 Paderborn, Germany.
J Neural Eng. 2021 Feb 25;18(2). doi: 10.1088/1741-2552/abd578.
Energy consumption is a critical issue in resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) has emerged as a powerful framework in addressing this issue owing to its highly efficient data compression procedure. In this paper, a CS-based approach termed simultaneous analysis non-convex optimization (SANCO) is proposed for large-scale, multi-channel local field potentials (LFPs) recording.The SANCO method consists of three parts: (1) the analysis model is adopted to reinforce sparsity of the multi-channel LFPs, therefore overcoming the drawbacks of conventional synthesis models. (2) An optimal continuous order difference matrix is constructed as the analysis operator, enhancing the recovery performance while saving both computational resources and data storage space. (3) A non-convex optimizer that can by efficiently solved with alternating direction method of multipliers is developed for multi-channel LFPs reconstruction.Experimental results on real datasets reveal that the proposed approach outperforms state-of-the-art CS methods in terms of both recovery quality and computational efficiency.Energy efficiency of the SANCO make it an ideal candidate for resource-constrained, large scale wireless neural recording. Particularly, the proposed method ensures that the key features of LFPs had little degradation even when data are compressed by 16x, making it very suitable for long term wireless neural recording applications.
能量消耗是资源受限的无线神经记录应用的一个关键问题，这些应用的数据带宽有限。压缩感知（CS）由于其高效的数据压缩过程，已成为解决这一问题的强大框架。在本文中，提出了一种基于 CS 的方法，称为同时分析非凸优化（SANCO），用于大规模、多通道局部场电位（LFPs）记录。SANCO 方法由三部分组成：（1）采用分析模型来增强多通道 LFPs 的稀疏性，从而克服传统合成模型的缺点。（2）构建最优连续阶差分矩阵作为分析算子，在节省计算资源和数据存储空间的同时提高恢复性能。（3）开发了一种可以通过交替方向乘子法有效求解的非凸优化器，用于多通道 LFPs 重建。在真实数据集上的实验结果表明，与最先进的 CS 方法相比，该方法在恢复质量和计算效率方面都具有优势。SANCO 的能量效率使其成为资源受限、大规模无线神经记录的理想选择。特别是，即使数据压缩 16 倍，该方法也能确保 LFPs 的关键特征几乎没有退化，非常适合长期无线神经记录应用。
