Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery

TL;DR

This paper demonstrates that domain adaptation techniques significantly improve the accuracy of classifying brain states from fMRI data during visual perception and mental imagery tasks, outperforming standard methods.

Contribution

It introduces a domain adaptation-enhanced searchlight approach for better brain state classification from fMRI data, with comprehensive analysis and publicly available code.

Findings

DA improves binary and multiclass imagery prediction accuracy

DA-enhanced searchlight identifies distributed brain regions involved in imagery

The method outperforms standard cross-domain classification techniques

Abstract

In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using primarily visual data from fMRI scans of 18 subjects. Initially, we train a baseline model on visual stimuli to predict imagined stimuli, utilizing data from 14 brain regions. We then develop several models to improve imagery prediction, comparing different DA methods. Our results demonstrate that DA significantly enhances imagery prediction in binary classification on our dataset, as well as in multiclass classification on a publicly available dataset. We then conduct a DA-enhanced searchlight analysis, followed by permutation-based statistical tests to identify brain regions where imagery decoding is consistently above chance across subjects. Our DA-enhanced searchlight predicts imagery contents in a highly distributed set of brain regions, including the visual cortex and the frontoparietal cortex, thereby outperforming standard cross-domain classification methods. The complete code and data for this paper have been made openly available for the use of the scientific community.