High-Dimensional Classification for Brain Decoding

TL;DR

This paper investigates advanced high-dimensional data analysis techniques like functional principal component analysis, mutual information networks, and persistent homology for brain decoding from MEG data, enhancing classification of cognitive states.

Contribution

It introduces a combined approach using these techniques with elastic net regularized multinomial logistic regression for improved brain decoding.

Findings

Effective feature extraction methods for high-dimensional neuroimaging data

Successful classification of video stimuli from MEG signals

Demonstrated the utility of topological data analysis in brain decoding

Abstract

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.