Bayesian Regression for Artifact correction in Electroencephalography

Many brain-computer interfaces (BCIs) measure brain activity using electroencephalography (EEG). Unfortunately, EEG is highly sensitive to artifacts originating from non-neural sources, requiring procedures to remove the artifactual contamination from the signal. This work presents a probabilistic interpretation for artifact correction that unifies session transfer of linear models and calibration to upcoming sessions. A linear artifact correction model is derived within a Bayesian multi-task learning (MTL) framework, which captures influences of artifact sources on EEG channels across different sessions to correct for artifacts in new sessions or calibrate with session-specific data. The new model was evaluated with a cross-correlation analysis on a real world EEG data set. We show that the new model matches stateof-the-art correlation reduction abilities, but ultimately converges to a simple group mean model for the experimental data set. This observation leaves the proposed MTL approach open for a more detailed investigations of artifact tasks.

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