Artificial Neural Networks for Magnetoencephalography: A review of an emerging field

Arthur Dehgan; Hamza Abdelhedi; Irina Rish; Karim Jerbi; Vanessa Hadid

REVIEW 3 major objections 6 minor 219 references

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This review identifies 119 studies using artificial neural networks on MEG data and organizes them into classification, modeling, and other methodological applications, arguing the field is growing rapidly.

desk verdict A useful first map of ANN-MEG, but the census undercounts from a narrow search query; fix the audit trail before treating the numbers as definitive. read the letter →

arxiv 2501.11566 v4 pith:4G5EHRW4 submitted 2025-01-20 q-bio.NC

Arthur Dehgan , Hamza Abdelhedi , Vanessa Hadid , Irina Rish , Karim Jerbi This is my paper · ORCID

classification q-bio.NC

keywords magnetoencephalographyartificialneuralnetworksdeeplearningbraindecodingrepresentationalsimilarityanalysissourcelocalizationartifactremovalsystematicreview

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review tries to establish that artificial neural networks have become a recognizable, rapidly growing subfield of MEG research, where MEG is magnetoencephalography, a technique that records magnetic fields from brain activity with millisecond timing. It reports 119 relevant studies, with classification dominant at 70 studies, modeling smallest at 16, and 33 studies in an 'Other' category covering preprocessing, source localization, and methods. If the review's picture is right, ANN-MEG work is not a scattering of one-off demonstrations but a coherent field with distinct pipelines, evaluation practices, and open challenges. The authors also claim the trajectory resembles the earlier expansion of deep learning in EEG analysis, which would predict continued growth, more foundation models, and multimodal decoding.

What carries the argument

The organizing device is the three-category taxonomy (Classification, Modeling, Other) with subcategories, applied to a corpus of 119 studies. This taxonomy does the argument's work: it turns a heterogeneous collection of papers into a map of pipelines, enabling the review's quantitative claims about growth, architecture choices, validation practices, baseline reporting, and gaps in interpretability.

What would settle it

Re-run the literature search from the methods section without requiring a digital object identifier and without the English-only restriction, recording screening counts at each stage; then recompute the total number of studies and the category shares in figures 2a and 2c. If the total grows substantially or the classification share drops below half, the review's portrait of the field depends on its search choices rather than on the underlying research activity.

Watch

Extended reading notes

Core claim

The central claim is that ANNs are being applied to MEG data in three distinct modes: classification, where MEG trials are inputs and outputs are labels for decoding, brain-computer interfaces, clinical diagnosis, or event detection; modeling, where ANN activations are compared with MEG responses to the same stimuli, mostly via representational similarity analysis or neural predictivity; and other methodological uses, including preprocessing, artifact removal, and source localization. The review further claims that classification pipelines are diverse with no standard protocol, that modeling studies are gaining momentum, and that the main bottlenecks are data scarcity, limited interpretability, and reproducibility.

Load-bearing premise

The load-bearing premise is that the 119 studies found by the English-only search, which required a digital object identifier, across the four databases described in the methods, accurately represent the whole body of ANN-MEG work; if relevant preprints without DOIs, non-English papers, or studies using different terminology were missed, the reported counts, category shares, and growth trend could shift.

Editorial extensions

If this is right

If the growth curve in figure 2a continues as the review expects, more MEG studies will adopt ANNs, and the field should see more foundation models for MEG, following the trajectory observed in EEG.
Classification research will likely remain CNN-dominated, but without shared benchmarks or standardized preprocessing, cross-study comparisons will stay difficult.
Modeling studies using RSA and neural predictivity may become a standard way to test whether ANN representations track the millisecond-scale dynamics of the human brain.
ANN-based source localization is promising but bounded by forward-model accuracy; gains over classical inverse methods such as MNE, beamforming, and sLORETA will depend on realistic simulations.
Interpretability tools, used in only 16 of 70 classification studies, will need to become routine for decoding claims to be explainable.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper does not claim this, but the DOI requirement likely biases the corpus toward published work; including DOI-less preprints could reveal a steeper recent growth curve and a larger share of 'Other' methodological studies.
If the taxonomy were applied to the EEG-ANN literature, tests of whether MEG's temporal resolution yields systematically different modeling insights could be made explicit.
Given that 27 of 70 classification studies lack baseline comparisons, a re-analysis could quantify how often ANN accuracies actually beat classical classifiers; the review leaves this as future work.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's my take on this review. It's the first systematic survey of artificial neural networks applied to MEG, organized around a sensible three-way taxonomy (classification, modeling, other) with subcategories, and it tables a lot of useful extracted metadata (subjects, trials, architectures, validation, code availability) for 119 studies. That alone makes it a useful entry point for someone entering the field. The practical recommendations—report class imbalance, use baselines, detail hyperparameters, publish code—are sensible and well grounded in what the corpus actually shows.

The soft spots are real. The search query in §2.1 is narrower than the field: it includes 'artificial neural network*' but not 'neural network', 'deep neural network', 'MLP', 'transformer', 'LSTM', or 'autoencoder'. Combined with a DOI-only inclusion rule, this will miss many relevant preprints and conference papers, and it biases the year-by-year curve toward recent, DOI-bearing work. The stress-test note lands: the census is likely an undercount and the growth trend in Fig. 2a is only indicative. The review also contains an internal inconsistency: §3.2.5 refers to '71 studies' where the category has 70 per the tables, and the count of classification studies missing training parameters is 17 in §3.2.5 but 16 in §4.3.4. These are minor numeric slips, but in a review built on numbers they need fixing.

The paper does not report PRISMA-style screening counts or a precise search date, so the audit trail is weaker than it should be for a quantitative census. If the authors can supply the screening flow, broaden the query, and reframe the 119/70/16/33 figures as approximate, the review will be much more durable.

Who's it for? Anyone wanting an overview of ANN-MEG applications, especially newcomers; the tables are a quick-reference digest. It deserves serious peer review—the gap is real and the organization is genuinely helpful. I'd recommend revision rather than desk rejection, with the search protocol and internal counts being the priority.

Referee Report

3 major / 6 minor

Summary. This review surveys the use of artificial neural networks (ANNs) in magnetoencephalography (MEG) research. The authors searched PubMed, Google Scholar, arXiv, and bioRxiv with a fixed query, screened titles, abstracts, and PDFs, and included 119 primary research articles with a DOI published before November 2024. They categorize the corpus into Classification (70 studies, further split into decoding, BCI, clinical, and event detection), Modeling (16 studies), and Other (33 studies, covering preprocessing, source localization, and methods). For each category, the review summarizes typical pipelines, data characteristics, network architectures, training and validation practices, and current limitations, and closes with recommendations on data augmentation, validation, baselines, interpretability, and reproducibility.

Significance. If the census is accurate, this is a valuable and timely reference: it organizes a rapidly growing literature into a clear taxonomy, provides condensed tables of architectures, datasets, and validation schemes, and identifies recurring methodological weaknesses (missing baselines, limited interpretability, sparse reporting of hyperparameters). The paper's main strengths are its breadth, the explicit categorization scheme, and the practical recommendations grounded in the corpus. The central quantitative claim—119 studies with a 70/16/33 split and a rapid growth trend—is, however, only as reliable as the literature search and the consistency of the reported counts, both of which have issues that need to be addressed.

major comments (3)

[§2.1, Fig. 1, Abstract] The search query omits several common terms for neural-network methods, including "neural network" (without the qualifier "artificial"), "deep neural network", "multilayer perceptron", "MLP", "autoencoder", "transformer", "LSTM", "GRU", "BERT", and "GPT". Because the initial candidate pool is defined by this query and then screened by title and abstract, a MEG study whose abstract says "we trained a deep neural network" or "a transformer-based model" would be missed. The included corpus itself contains papers such as [163] (a GPT model), [159] (ROCKET-based), and [165] (CLIP-based) that rely on terminology outside the stated query terms, so the concern is concrete. The PDF search described in §2.1 does not repair this, since the query defines the pool before PDF screening. This directly affects the central 119-study count and the growth trajectory in Fig. 2a. I ask the authors to rerun the search with an expanded term set, report the exact search date and screening counts (including how many titles/abstracts were screened at each stage), and discuss how the DOI-only inclusion criterion affects coverage of older conference papers and preprints, which may bias the year-by-year curve toward recent years.
[§3.2.5, §4.3.4, Table 6, §3.3.2] The manuscript contains several internal numeric inconsistencies that affect the quantitative portrait. Specifically: §3.2.5 states "Among the 71 studies in this category" though the Classification category contains 70 studies per Tables 2–3 and §3.2.2; §4.3.4 says "16 of the classification studies did not provide enough information" about hyperparameters, while §3.2.5 says 17 studies do not mention training parameters; Table 6 lists reference [165] under both "Methods" and "Source localization", and the Source localization subcategory omits [141] that is listed in Table 5; and §3.3.2 refers to "the 11 studies using RSA" while §3.3.3 and the preceding text in §3.3.1 describe 10 RSA-based studies. These discrepancies are individually small but collectively undermine confidence in the reported statistics. The authors should reconcile all counts across the text, tables, and figure captions.
[§4.2, Table 2] A few citation/count errors also appear in the discussion: §4.2 lists [119] as an example of a classification study, but [119] is a Modeling study; and Table 2 lists the Shu and Fyshe study [77] (a 2013 workshop paper) under publication year 2020. Such errors, while not central to the main thesis, should be corrected to make the review a reliable reference.

minor comments (6)

[§2.1] The section heading contains a typo: "Litterature research" should be "Literature research".
[§1.6] The sentence "These techniques have proven to be shown to be useful" is grammatically awkward and should be rewritten.
[§3.2.3] The sentence about sampling frequencies reports both a median of 250 Hz in §3.1 and a median of 600 Hz in §3.2.3; please make these consistent and clarify what set each median is computed over.
[§3.3.1] The text says "Out of the eleven studies investigating the visual cortex (including visual word recognition)", but the earlier enumeration gives nine visual-cortex studies plus one visual-word-recognition study, i.e., ten. Please correct the number.
[Table 6] In the Source localization row, reference [165] appears to be misassigned (it is a multimodal alignment study, not a source-localization study), and [141] is missing from that row.
[§4.3.7] The sentence "Roughly half of the 'Methods' studies (9 out of 16) included visualization techniques" is internally consistent, but it would be clearer to say "9 of 16" rather than "roughly half".

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the aggregate census is built from 119 external primary studies, and the few author self-citations are advisory rather than load-bearing.

full rationale

This is a review article, not a derivation; its central quantitative claims are the count of 119 studies, the 70/16/33 category split, and the growth trend in Figure 2a. These are presented as aggregates of independently published primary studies collected via the search described in Section 2.1, so the census is external evidence rather than a restatement of the authors' own inputs. The paper contains no equations in which an output is defined in terms of itself, no fitted parameter is renamed as a prediction, and no uniqueness theorem is invoked from the authors' prior work. The only self-citations are [184] (a class-imbalance methods paper by some of the same authors) and [219] (an open-source MEG-CNN library by the authors), cited respectively for a recommendation about imbalance mitigation and as an example of multimodal MEG-EEG frameworks. These citations support practical suggestions and illustrative examples, not the aggregate statistics or the main conclusions; even if those citations were removed, the 119-study census and category distributions would be unchanged. The skeptic's concern that the Section 2.1 query omits terms such as 'neural network,' 'deep neural network,' 'transformer,' or 'LSTM' is a legitimate validity and coverage risk for the completeness of the census, but it is a bias/under-counting concern rather than a circularity: the reported counts are still derived from external papers, not from the authors' own outputs or definitions. Similarly, the DOI requirement could bias the year-by-year curve toward recent years, but that is a selection-bias issue, not a self-referential reduction. The categorization into Classification/Modeling/Other is organizational and does not smuggle in a conclusion by definition: each category is populated from the external corpus and the splits are reported as observations. Overall, no circular step was found, and the paper's central content is self-contained against external benchmarks in the sense appropriate for a literature survey.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The review introduces no free parameters, no fitted constants, and no invented entities. Its conclusions rest on the bibliographic and classification assumptions listed above, which are standard for a literature review but should be auditable.

assumptions (4)

domain assumption The search query, database selection, and screening process (Section 2.1, Figure 1) yield a comprehensive and representative corpus of ANN-MEG studies.
The quantitative portrait (119 studies, category counts, figures 2-3) depends on this assumption; no PRISMA-style counts or an exact search date are reported to validate coverage.
domain assumption The three-category taxonomy (Classification, Modeling, Other) and its subcategories faithfully capture the intellectual structure of the field.
Section 2.2 defines the categories; distribution claims (figure 2c,d) rely on subjective grouping, and some studies are discussed across categories (e.g., Cichy et al. [119] appears in both modeling and decoding contexts).
domain assumption Information reported in the primary studies (subject counts, trial counts, preprocessing steps, validation methods) is accurate and complete enough to support the review's summary statistics.
Sections 3.1-3.4 aggregate numbers from the 119 papers; the review itself notes that many papers omit details, and uses 'N/M' for missing values rather than verifying the original data.
domain assumption Requiring a valid DOI and English full text does not systematically exclude relevant ANN-MEG work.
Section 2.1 imposes a DOI requirement and English-language criterion; preprints without DOIs or studies published in other languages are excluded, which could bias the corpus.

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Cite this review

Pith. "Pith review of Artificial Neural Networks for Magnetoencephalography: A review of an emerging field." pith.science (2026). https://pith.science/paper/4G5EHRW4

@misc{pith2026250111566,
  author       = {Pith},
  title        = {Pith review of: Artificial Neural Networks for Magnetoencephalography: A review of an emerging field},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4G5EHRW4}},
  note         = {Machine review of arXiv:2501.11566}
}

read the original abstract

Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive processes with an unparalleled combination of high temporal and spatial precision. MEG data analytics has always relied on advanced signal processing and mathematical and statistical tools for various tasks ranging from data cleaning to probing the signals' rich dynamics and estimating the neural sources underlying the surface-level recordings. Like in most domains, the surge in Artificial Intelligence (AI) has led to the increased use of Machine Learning (ML) methods for MEG data classification. More recently, an emerging trend in this field is using Artificial Neural Networks (ANNs) to address many MEG-related tasks. This review provides a comprehensive overview of how ANNs are being used with MEG data from three vantage points: First, we review work that employs ANNs for MEG signal classification, i.e., for brain decoding. Second, we report on work that has used ANNs as putative models of information processing in the human brain. Finally, we examine studies that use ANNs as techniques to tackle methodological questions in MEG, including artifact correction and source estimation. Furthermore, we assess the current strengths and limitations of using ANNs with MEG and discuss future challenges and opportunities in this field. Finally, by establishing a detailed portrait of the field and providing practical recommendations for the future, this review seeks to provide a helpful reference for both seasoned MEG researchers and newcomers to the field who are interested in using ANNs to enhance the exploration of the complex dynamics of the human brain with MEG.

Figures

Figures reproduced from arXiv: 2501.11566 by the authors.

**Figure 1.** Schematic overview of the literature review process. An initial search query across specified databases yielded [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗

**Figure 2.** Quantitative overview of the reviewed studies (n=119). (a) The evolution of the number of MEG [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

**Figure 3.** Distribution of sampling frequencies (in Hz) reported across the reviewed studies (N=110 reporting). [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Representative workflows for applying ANNs to MEG data across the main application categories [PITH_FULL_IMAGE:figures/full_fig_p015_4.png]

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