This project explores how the brain responds to natural stories via TRF modeling on the SMN4Lang dataset, using acoustic envelope and word-aligned features – and includes an exploratory attempt at word classification using machine learning.
Word processing is influenced by different attributes of words, which can be observed vis brain imaging techniques such as MEG. Let’s find evidence of the influence of word attributes by analyzing a dataset.
In this project I employed some of the tools we learned at the Brainhack school to generate interactive figures to display functional connectivity from MEG and fMRI resting state data from the Human Connectome Project.
Copyright (c) 2025, BrainHack School; all rights reserved.
Template by Bootstrapious. Ported to Hugo by DevCows.