OPM-MEG bad channel identification method based on the improved box-isolation forest algorithm

Abstract

Bad channels result from the inherent instability of wearable optically pumped magnetometers (OPMs). These unreliable channels significantly impede subsequent signal analysis. The signal space separation method for OPMs, which relies on time-domain features such as the z-score, cannot accurately identify these unreliable channels. We introduce a novel approach for automatic bad channel identification in OPMs, utilizing frequency domain features. Box-plot technique enables predicting the number of unreliable channels. The predicted ratio of unreliable channels is incorporated as a crucial parameter in the Isolation Forest algorithm to achieve the automated identification of bad channels. To evaluate the effectiveness of our method, we employed three accuracy metrics and conducted comparative assessments via the following methods: box-plot, density-based spatial clustering of applications with noise, and local outlier factor. Our approach is validated via simulation experiments and real measurements using OPM-magnetoencephalography data. The efficiency of subsequent OPM-MEG preprocessing and source location analysis is improved.