# Adaptive neural network classifier for decoding MEG signals

arXiv: https://arxiv.org/abs/1805.10981 · HTML: https://mlsanity.com/paper/1805.10981/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2018.

## Built on by (most cited first, 3 of 3)

- [fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding](https://mlsanity.com/paper/2311.00342/) (2023)
- [Group-level Brain Decoding with Deep Learning](https://mlsanity.com/paper/2205.14102/) (2022)
- [Interpretable MEG Decoding of Perceived Speech: Cortical Sources and the Stimulus Features That Drive Retrieval](https://mlsanity.com/paper/2608.01481/) (2026)

## Abstract (arXiv)

Convolutional Neural Networks (CNN) outperform traditional classification methods in many domains. Recently these methods have gained attention in neuroscience and particularly in brain-computer interface (BCI) community. Here, we introduce a CNN optimized for classification of brain states from magnetoencephalographic (MEG) measurements. Our CNN design is based on a generative model of the electromagnetic (EEG and MEG) brain signals and is readily interpretable in neurophysiological terms. We show here that the proposed network is able to decode event-related responses as well as modulations of oscillatory brain activity and that it outperforms more complex neural networks and traditional classifiers used in the field. Importantly, the model is robust to inter-individual differences and can successfully generalize to new subjects in offline and online classification.