# Across-subjects classification of stimulus modality from human MEG high frequency activity
Britta U. Westner, Sarang S. Dalal, Simon Hanslmayr, & Tobias Staudigl
Single-trial analyses have the potential to uncover meaningful brain dynamics that are
obscured when averaging across trials. However, low signal-to-noise ratio (SNR) can
impede the use of single-trial analyses and decoding methods. In this study, we
investigate the applicability of a single-trial approach to decode stimulus modality from
magnetoencephalographic (MEG) high frequency activity. In order to classify the
auditory versus visual presentation of words, we combine beamformer source
reconstruction with the random forest classification method. To enable group level
inference, the classification is embedded in an across-subjects framework.
We show that single-trial gamma SNR allows for good classification performance
(accuracy across subjects: 66.44 %). This implies that the characteristics of high
frequency activity have a high consistency across trials and subjects. The random forest
classifier assigned informational value to activity in both auditory and visual cortex
with high spatial specificity. Across time, gamma power was most informative during
stimulus presentation. Among all frequency bands, the 75 Hz to 95 Hz band was the
most informative frequency band in visual as well as in auditory areas. Especially in
visual areas, a broad range of gamma frequencies (55 Hz to 125 Hz) contributed to the
Thus, we demonstrate the feasibility of single-trial approaches for decoding the
stimulus modality across subjects from high frequency activity and describe the
discriminative gamma activity in time, frequency, and space.
This repository contains the data needed to re-create the analyses and figures from the paper.
The code is hosted on GitHub and linked here as well.