ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features

TLDR

Independent Component Analysis (ICA) is a common method for removing artifacts from EEG recordings, yet its implementation is largely user‑dependent. The authors propose ADJUST, a fully automatic algorithm that identifies artifacted independent components by combining stereotyped artifact‑specific spatial and temporal features. ADJUST optimizes these spatial and temporal features to detect blinks, eye movements, and generic discontinuities using a feature selection dataset. Validation on an independent EEG dataset shows that ADJUST’s component classification agrees with expert manual labeling on 95.2% of data variance and that removing the identified components yields clean visual and auditory event‑related potentials, demonstrating a fast, efficient, automatic ICA‑based artifact removal method.

Abstract

A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user-dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST's classification of independent components largely matches a manual one by experts (agreement on 95.2% of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory event-related potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal.