Artifact-reference multivariate backward regression (ARMBR): a novel method for EEG blink artifact removal with minimal data requirements

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Abstract Objective . We present a novel and lightweight method that removes ocular artifacts from electroencephalography (EEG) recordings while demanding minimal training data. Approach . A robust, cross-validated thresholding procedure automatically detects the times at which eye blinks occur, then a linear scalp projection is estimated by regressing a simplified time-locked reference signal against the multi-channel EEG. Main results . Performance was compared against four commonly-used and readily available blink removal methods: signal subspace projection and forward regression (Reg) from the MNE toolbox, EEGLab’s independent component analysis (ICA) combined with ICLabel for automated component identification, and Artifact Subspace Reconstruction (ASR) Python implementation compatible with MNE. On semi-synthetic blink-contaminated EEG data, our method exhibited better reconstruction of the ground truth than the two MNE native methods, and comparable (or better in some scenarios) performance to ASR algorithm and ICA+IClabel. We also examined a real EEG dataset from 16 human participants, where the ground truth was unknown. Our method affected contaminated channels in blink intervals more than the two MNE native methods and ASR, while having a smaller impact on non-blink intervals, uncontaminated channels, and higher-frequency amplitudes, than the two MNE methods; its performance was again similar to ICA+ICLabel. On a second real dataset from 42 human participants, we showed that ARMBR removed the unwanted blink artifacts while successfully preserving the desired event-related-potential signals. Significance . The proposed algorithm exhibited comparable, and in some scenarios better performance relative to readily-available implementations of other widely-used methods. Another feature of our method is its potential as method for online applications. Therefore, it stands to make valuable contributions towards the automation of neural-engineering technologies and their translation from laboratory to clinical and other real-world usage.

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