A Coding Implementation of End-to-End Brain Decoding from MEG Signals Using NeuralSet and Deep Learning for Predicting Linguistic Features

A tutorial demonstrates end-to-end brain decoding of word length from MEG signals.

It covers installing dependencies, loading a MEG study, and building a structured data pipeline with NeuralSet and NeuralFetch.

The workflow includes a custom feature extractor and a convolutional neural network to predict linguistic information from brain responses.

The example highlights how neural activity can be translated into language-related predictions for neuroAI and brain-computer interface research.