Enhancing multivariate pattern analysis for magnetoencephalography through relevant sensor selection

Abstract

AbstractWhile the popularity of multivariate pattern classification is growing rapidly in magnetoencephalography (MEG) data analysis, the analysis pipelines used by the neuroscience community are still missing some fundamental machine‐learning techniques and principles that would increase their effectiveness. Here, we show that MEG decoding accuracy improves significantly with the addition of feature selection methods to the analysis pipeline. We compare one unsupervised and two supervised feature reduction methods in the current study. Our results show that supervised feature selection methods like statistical dependency and mutual information improve decoding performance and attain higher session‐to‐session reliability compared to unsupervised dimensionality reduction methods like principal component analysis. Furthermore, we demonstrate that the selected sensors in the data related to a visual task at each time point are consistent with the pattern reflecting the sweep of information in the ventral visual pathway.