Hardware-Oriented Memory-Limited Online Artifact Subspace Reconstruction (HMO-ASR) Algorithm
Hardware-Oriented Memory-Limited Online Artifact Subspace Reconstruction (HMO-ASR) Algorithm
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DOI：
10.1109/tcsii.2021.3124253
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发表时间：
2021-12-01
影响因子：
4.4
通讯作者：
Jung, Tzyy-Ping
中科院分区：
文献类型：
--
作者：
Van, Lan-Da;Tu, You-Cheng;Jung, Tzyy-Ping
伪影重建是一种机器学习技术，广泛用于从脑电中去除非脑信号(称为伪影)。然而，ASR算法可能会受到便携设备上可用内存有限的限制。为了应对这一挑战，我们提出了一种面向硬件的内存受限在线ASR(HMO-ASR)算法。提出的HMO-ASR算法包括：(1)基于主成分分析和基于z-Score的两级窗口预处理以清理每个窗口中的数据；(2)使用并行算法迭代均值、标准差和协方差更新以实现基于窗口的处理；(3)提前确定特征向量矩阵以节省计算量。通过这三种方案，HMO-ASR方法可以在移动设备、专用集成电路(ASIC)或存储空间有限的现场可编程门阵列(FGA)上实现。研究结果表明，提出的HMO-ASR算法可以获得与离线ASR算法相当的性能，而内存大小减少了98.64%。使用一个FPGA实现对所提出的HMO-ASR算法进行了硅片验证。
Artifact Subspace Reconstruction (ASR) is a machine learning technique widely used to remove non-brain signals (referred to as "artifacts") from electroencephalograms (EEGs). The ASR algorithm can, however, be constrained by the limited memory available on portable devices. To address this challenge, we propose a Hardware-Oriented Memory-Limited Online ASR (HMO-ASR) algorithm. The proposed HMO-ASR algorithm consists of (1) two-level window-based preprocessing including PCA-based and z-score-based preprocessing to clean the data in each window, (2) iterative mean, standard deviation, and covariance update using a parallel algorithm to achieve window-based processing, and (3) early eigenvector matrix determination to save the computation. With the three schemes, the HMO-ASR method can be implemented on mobile devices, application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) with limited memory. The study results showed that the proposed HMO-ASR algorithm can achieve comparable performance to those obtained by the offline ASR algorithm with a 98.64% reduction in memory size. An FPGA implementation is used for silicon proof of the proposed HMO-ASR algorithm.