Hardware-Oriented Memory-Limited Online Artifact Subspace Reconstruction (HMO-ASR) Algorithm

Lan Da Van*, You Cheng Tu, Chi Yuan Chang, Hsiang Jen Wang, Tzyy Ping Jung

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Pages (from-to)3493-3497
Number of pages5
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Issue number12
StatePublished - 1 Dec 2021


  • and EEG
  • Artifact subspace reconstruction
  • hardware-oriented
  • limited memory


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