Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal

By Chi-Yuan Chang, Sheng-Hsiou Hsu, Luca Pion-Tonachini, Tzyy-Ping Jung. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018

299 citations recorded by the source corpus.

One of the greatest challenges that hinder the decoding and application of electroencephalography (EEG) is that EEG recordings almost always contain artifacts - non-brain signals. Among existing automatic artifact-removal methods, artifact subspace reconstruction (ASR) is an online and realtime capable, component-based method that can effectively remove transient or large-amplitude artifacts. However, the effectiveness of ASR and the optimal choice of its parameter have not been evaluated and reported, especially on real EEG data. This study systematically validates ASR on ten EEG recordings in a simulated driving experiment. Independent component analysis (ICA) is applied to separate artifacts from brain signals to allow a quantitative assessment of ASR's effectiveness in removing various types of artifacts and preserving brain activities. Empirical results show that the optimal ASR parameter is between 10 and 100, which is small enough to remove activities from artifacts and eye-related components and large enough to retain signals from brain-related components. With the appropriate choice of the 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, Signal Processing

View Paper: https://www.semanticscholar.org/paper/10115532ef40e1df7557cdf347c25575e1df9ce4

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Citations in the corpus

Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings (2020, methodology): “Next, extending from the previous study [32], this study applies ICA and further employs a recently-developed, automatic IC classifier (ICLabel) [33] to separately identify brain activities and various types of artifact signals.”
Temporal and topographical changes in theta power between middle childhood and adolescence during sentence comprehension (2021, methodology): “Bad electrodes and blocks of data containing muscle activity/artifacts were removed from the continuous EEG data by using the artifact subspace reconstruction plug-in within EEGlab (Mullen et al., 2015; Chang et al., 2018).”