Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings

By Chi-Yuan Chang, Sheng-Hsiou Hsu, Luca Pion-Tonachini, Tzyy-Ping Jung. IEEE Transactions on Biomedical Engineering, 2020

471 citations recorded by the source corpus.

Objective: Artifact subspace reconstruction (ASR) is an automatic, online-capable, component-based method that can effectively remove transient or large-amplitude artifacts contaminating electroencephalographic (EEG) data. However, the effectiveness of ASR and the optimal choice of its parameter have not been systematically evaluated and reported, especially on actual EEG data. Methods: This paper systematically evaluates ASR on 20 EEG recordings taken during simulated driving experiments. Independent component analysis (ICA) and an independent component classifier are applied to separate artifacts from brain signals to quantitatively assess the effectiveness of the ASR. Results: ASR removes more eye and muscle components than brain components. Even though some eye and muscle components retain after ASR cleaning, the power of their temporal activities is reduced. Study results also showed that ASR cleaning improved the quality of a subsequent ICA decomposition. Conclusions: Empirical results show that the optimal ASR parameter is between 20 and 30, balancing between removing non-brain signals and retaining brain activities. Significance: With an appropriate choice of parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain–computer interfaces.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/5b7f65fbef6673a7c98619caa380977d6ad64077

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In Cognitive Neuroscience

Citations in the corpus

EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, methodology): “A recent study in 2019 [46] also considered ICA and used an automatic IC classifier as a quantitative measure to separate brain signals and artefacts for signal enhancement.”
RELAX‐Jr: An Automated Pre‐Processing Pipeline for Developmental EEG Recordings (2024, unknown): “For example, the SER:ARR graph suggests ASR is superior to APICE, as ASR has a higher SER with approximately the same ARR value.”
Examination of distraction and discomfort caused by using glare monitors: a simultaneous electroencephalography and eye-tracking study (2023, methodology): “First, a 1 Hz high-pass filter was applied, and then Artifact Subspace Reconstruction (Chang et al., 2020) was used to correct for noisy data.”