High-Dimensional Classification for Brain Decoding
This work addresses brain decoding for neuroscience and medical applications, but it is incremental as it integrates existing methods without introducing a fundamentally new approach.
The paper tackles the problem of classifying cognitive states from high-dimensional brain imaging data by combining functional principal component analysis, mutual information networks, and persistent homology for feature extraction, and applies a symmetric multinomial logistic regression with elastic net regularization to infer video stimuli from MEG data, achieving competitive performance in brain decoding.
Brain decoding involves the determination of a subject's cognitive state or an associated stimulus from functional neuroimaging data measuring brain activity. In this setting the cognitive state is typically characterized by an element of a finite set, and the neuroimaging data comprise voluminous amounts of spatiotemporal data measuring some aspect of the neural signal. The associated statistical problem is one of classification from high-dimensional data. We explore the use of functional principal component analysis, mutual information networks, and persistent homology for examining the data through exploratory analysis and for constructing features characterizing the neural signal for brain decoding. We review each approach from this perspective, and we incorporate the features into a classifier based on symmetric multinomial logistic regression with elastic net regularization. The approaches are illustrated in an application where the task is to infer, from brain activity measured with magnetoencephalography (MEG), the type of video stimulus shown to a subject.