MEG Neural Decoding Pipeline: The Issues Residing Within The Data and Methods to Improve Your Decoding Accuracy.

Overview

Authors: Dmitry Patashov^1,2, Li Liu³, Jion Tominaga⁴, Kai Nakajima⁵, Hiroki Miyanaga⁶, Shoji Tsunematsu⁷, Takanori Kato⁸, Keita Tanaka⁹, Hiromu Sakai^1,4

ORCID iDs: Dmitry Patashov

Institutions: Waseda University (Japan); RIKEN Center for Brain Science (Japan); RIKEN (Japan); Sumitomo Heavy Industries (Japan) (Japan); Tokyo Denki University (Japan)

Journal: Annals of biomedical engineering, volume 54, issue 8, pages 2450-2468

Dates: received 2 July 2025; accepted 15 March 2026; published online 9 April 2026; in print 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1007/s10439-026-04093-x · PMID 41954685 · PMCID PMC13391774 · OpenAlex W7152842526

Open access: hybrid, a free copy (OpenAlex)

Status: data only

Categories: MEG (modality), human (organism), methods / tools (subfield)

Methods: Spectral & time-frequency, Connectivity, Smoothing, state filtering, decompositions, Machine learning, Statistics, Preprocessing

Keywords: Biomagnetism, Signal processing, Neural decoding, Machine learning, Data augmentation, CA, MEG, EMD, PCA

MeSH: Machine Learning*, Magnetoencephalography*, Signal Processing, Computer-Assisted*, Algorithms, Humans (* major topic)

Topic: Generative Adversarial Networks and Image Synthesis (Computer Vision and Pattern Recognition, Computer Science), according to OpenAlex

Funding: Japan Society for the Promotion of Science (JP 23K17272, JP 23H05493)

Citations: not cited yet (Europe PMC); 35 references in the paper

Abstract

This study suggests a new analysis pipeline of MEG data, uniquely designed for neural decoding of small-sized datasets. It combines classic methods that assume stationarity of the data together with non-stationary methods to compensate for the distortions created by the classic approach. Popular Fourier-based methods are applied in a classic fashion, followed by additional filters using empirical mode decomposition and principal component analysis to further clean the data. An automated approach for epoch rejection is proposed as well. In this work, we propose a novel approach for data augmentation. Unlike most other solutions, combinations’ averaging technique can be used on real data rather than synthetic one, making it more reliable from the neuroscientific point of view. It is also shown that this approach does not create any unnatural patterns within the augmented data. The proposed approach allows for application of machine learning algorithms on small-sized datasets. This broadens the list of available analyses for datasets with limited number of recorded examples. An image naming task was used for in-subject neural decoding estimations. In this work, we propose and compare four different machine learning designs. It is shown that a careful selection of the used channels, reduction of the feature dimensions, and averaging of the recorded epochs may significantly increase the accuracy of neural decoding.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

Datasets cited

zenodo:15789568 — at Zenodo; found in DataCite
zenodo:15789569 — at Zenodo; found in DataCite

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 9 keywords, 5 MeSH terms, 1 funder, 32 references.

Cite

This paper

Patashov, D., Liu, L., Tominaga, J., Nakajima, K., Miyanaga, H., Tsunematsu, S., Kato, T., Tanaka, K., & Sakai, H. (2026). MEG Neural Decoding Pipeline: The Issues Residing Within The Data and Methods to Improve Your Decoding Accuracy. Annals of biomedical engineering, 54(8), 2450-2468. https://doi.org/10.1007/s10439-026-04093-x

BibTeX

@article{patashov2026meg, author = {Patashov, Dmitry and Liu, Li and Tominaga, Jion and Nakajima, Kai and Miyanaga, Hiroki and Tsunematsu, Shoji and Kato, Takanori and Tanaka, Keita and Sakai, Hiromu}, title = {{MEG Neural Decoding Pipeline: The Issues Residing Within The Data and Methods to Improve Your Decoding Accuracy}}, journal = {Annals of biomedical engineering}, year = {2026}, month = apr, volume = {54}, number = {8}, pages = {2450--2468}, publisher = {Springer Science+Business Media}, issn = {0090-6964}, doi = {10.1007/s10439-026-04093-x}, url = {https://doi.org/10.1007/s10439-026-04093-x}, pmid = {41954685}, pmcid = {PMC13391774} }

RIS

TY - JOUR AU - Patashov, Dmitry AU - Liu, Li AU - Tominaga, Jion AU - Nakajima, Kai AU - Miyanaga, Hiroki AU - Tsunematsu, Shoji AU - Kato, Takanori AU - Tanaka, Keita AU - Sakai, Hiromu TI - MEG Neural Decoding Pipeline: The Issues Residing Within The Data and Methods to Improve Your Decoding Accuracy T2 - Annals of biomedical engineering J2 - Ann Biomed Eng PY - 2026 DA - 2026/04/09 VL - 54 IS - 8 SP - 2450 EP - 2468 SN - 0090-6964 PB - Springer Science+Business Media DO - 10.1007/s10439-026-04093-x UR - https://doi.org/10.1007/s10439-026-04093-x LA - en ER -

CSL-JSON

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