Artifact Subspace Reconstruction (ASR) for electroencephalography artifact removal must be optimized for each unique dataset

Artifact subspace reconstruction (ASR) is an automatic artifact reject method that can effectively remove transient or large-amplitude artifacts found in electroencephalographic (EEG) data. There is little systematic evidence on the effective parameter choice of ASR in real EEG data. No existing study has evaluated ASR’s performance in functional connectivity analysis, such as renormalized Partial Directed Coherence (rPDC). This paper systematically evaluates ASR on 31 EEG recordings taken during a source episodic memory retrieval task. Independent component analysis (ICA) and an independent component classifier, ICLabel, are applied to separate artifacts from brain signals to quantitatively assess the effectiveness of ASR. The effectiveness of ASR was quantified on the following metrics: the number of dipolar independent components, model order for multivariate autoregressive modeling, and the number of preserved trials. Results showed that ASR is either as effective or more effective than manual rejection of artifacts. Contrary to previous literature, the present study shows that the optimal ASR parameter could be substantially higher than 20 to 30 and could be as high as 120, depending on experimenter decisions for what to preserve. As such, ASR parameter choice should be justified in each study using quantitative preliminary analysis. This is the first study to systematically analyze ASR’s effectiveness in rPDC-based functional connectivity research. NOTE: This is the first draft; several methodological changes might occur at a later time upon further analysis.