Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model

Ay\c{s}e Bet\"ul Y\"uce; Chris Joey Leffler; Myra Spiliopoulou; Sarun Varghese; Sebastian Stober

arxiv: 2606.30104 · v1 · pith:DZSDY56Vnew · submitted 2026-06-29 · 💻 cs.AI

Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model

Ay\c{s}e Bet\"ul Y\"uce , Chris Joey Leffler , Sarun Varghese , Myra Spiliopoulou , Sebastian Stober This is my paper

Pith reviewed 2026-06-30 05:54 UTC · model grok-4.3

classification 💻 cs.AI

keywords EEG foundation modelstemporal feature extractiontime-series foundation modelstransfer learningmotor imageryemotion recognitionpretrained modelsfrozen encoders

0 comments

The pith

A pretrained general-purpose time-series model works as an effective frozen temporal feature extractor inside EEG foundation models.

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

The paper runs a controlled comparison inside one shared EEG foundation model architecture, swapping only the temporal feature extractor among a linear baseline, a convolutional encoder, and a frozen pretrained time-series model called MOMENT. On motor-imagery data the simpler extractors compete well, while emotion recognition improves with the richer pretrained representations. The central finding is that the off-the-shelf TSFM transfers useful temporal structure to EEG signals without any EEG-specific pretraining or fine-tuning of its weights. A reader should care because the result points to a practical shortcut: large time-series models trained on other domains can be dropped in as modular components rather than requiring every EEG model to start from random weights.

Core claim

Within a single unified EEG foundation model, replacing the temporal feature extractor with a frozen pretrained TSFM yields downstream performance that is competitive on motor imagery and advantageous on emotion recognition, showing that general time-series representations can be reused directly as frozen temporal modules for brain-signal modeling.

What carries the argument

The frozen pretrained TSFM (MOMENT) inserted as the temporal feature extractor inside the otherwise fixed EEG foundation model architecture.

If this is right

Simple linear temporal representations are sufficient for motor imagery classification under the tested conditions.
Emotion recognition tasks gain from the richer temporal modeling supplied by the pretrained extractor.
General-purpose time-series pretraining can be reused without EEG-specific adaptation when kept frozen.
Task-dependent choice of temporal extractor is required rather than a single strategy for all EEG benchmarks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the transfer holds, EEG foundation model training could be accelerated by borrowing scale from existing time-series corpora instead of collecting ever-larger EEG-only datasets.
Freezing the TSFM leaves open the question of whether light fine-tuning on EEG data would produce further gains without destroying the transferred representations.
The same modular replacement strategy could be tested on other biosignals such as ECG or MEG to check whether the pattern generalizes beyond EEG.

Load-bearing premise

The single shared EEG foundation model backbone keeps all other design choices identical so that performance differences can be attributed only to the choice of temporal extractor.

What would settle it

Re-running the exact same unified architecture but with the TSFM weights replaced by random initialization and finding that downstream accuracy on both tasks stays within a few percent of the pretrained version would falsify the claim that the pretraining itself supplies useful transferable temporal features.

Figures

Figures reproduced from arXiv: 2606.30104 by Ay\c{s}e Bet\"ul Y\"uce, Chris Joey Leffler, Myra Spiliopoulou, Sarun Varghese, Sebastian Stober.

**Figure 1.** Figure 1: Overview of the pretraining EEG foundation model. The top block presents the pretraining pipeline. The bottom-left (blue) block illustrates the temporal feature extractor variants, and the bottom-right (yellow) block shows the Spherical Positional Encoding. padding and providing a corresponding input mask. We use mean reduction over the internal patch representations of MOMENT, where internal patch embeddi… view at source ↗

**Figure 2.** Figure 2: Pretraining loss curves (train and validation) across 30 epochs for three model variants: (a) Linear, (b) Conv temporal feature extractor, and (c) MOMENT-small feature extractor. (a) Linear (b) Conv (c) MOMENT (d) Linear (e) Conv (f) MOMENT [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 3.** Figure 3: Reconstruction comparison for masked (top row) and unmasked (bottom row) tokens across three model variants: Linear, convolutional temporal feature extractor, and MOMENT-small temporal feature extractor. For each subplot, the upper plot shows the target and reconstructed signals, while the lower plot shows the absolute error between the target and reconstructed signal. 10 [PITH_FULL_IMAGE:figures/full_fig… view at source ↗

read the original abstract

Electroencephalography (EEG) foundation models aim to learn generalizable representations from large-scale brain recordings. However, the role of temporal feature extractors and whether pretrained time-series foundation models (TSFMs) can be effectively transferred to this setting remains underexplored. We conduct a controlled comparison of three temporal feature extraction strategies, including a linear baseline, a convolutional encoder, and a frozen pretrained TSFM (MOMENT), within a unified EEG foundation model. We evaluate their impact on representation quality using two downstream tasks: motor imagery and emotion recognition. Results reveal different trends across the evaluated benchmarks. On the motor imagery dataset, simple temporal representations perform competitively, whereas the emotion dataset benefits from richer temporal modeling. Although not specifically adapted to EEG, the pretrained TSFM serves as an effective temporal feature extractor, suggesting that general-purpose time-series representations can be transferred as frozen temporal feature extractors within EEG foundation models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper shows a frozen MOMENT TSFM can act as a usable temporal extractor in EEG models on emotion recognition but adds little on motor imagery, with the main open question being whether the three extractors were truly compared under identical conditions.

read the letter

The core finding is that general time-series pretraining transfers reasonably to EEG when used frozen, at least for the emotion task, while simple linear or conv extractors hold up fine on motor imagery. That controlled inclusion of MOMENT inside one EEG foundation model setup is the actual new piece; prior work has not done this side-by-side on these benchmarks.

The paper does the right thing by keeping the rest of the architecture fixed and testing two quite different downstream tasks. That already gives a clearer picture than most EEG foundation model papers that only report one task or one extractor.

The soft spot is the lack of any numbers, error bars, or statistical tests in the abstract, so it is impossible to judge how large the differences actually are or whether they survive multiple-testing correction. The stress-test note about possible hidden differences in how MOMENT is wired in (resampling, channel projection, patch handling) is worth checking in the methods section; if those steps are not byte-for-byte matched to the linear and conv paths, the transfer claim weakens.

This is for groups already building or evaluating EEG foundation models who want to know whether off-the-shelf time-series models are worth plugging in. It is not yet a definitive result because of the missing quantitative detail, but the question it asks is legitimate and the design is straightforward enough that a serious referee could evaluate it quickly.

I would send it to review rather than desk-reject; the empirical comparison is worth referee time even if the final numbers turn out modest.

Referee Report

2 major / 0 minor

Summary. The manuscript conducts a controlled comparison of three temporal feature extraction strategies (linear baseline, convolutional encoder, and frozen pretrained TSFM MOMENT) integrated into a unified EEG foundation model. It evaluates their effects on representation quality for motor imagery and emotion recognition downstream tasks, reporting that simple temporal representations are competitive on motor imagery while richer modeling benefits emotion recognition, and concluding that the pretrained TSFM serves as an effective transferable temporal feature extractor despite lacking EEG-specific adaptation.

Significance. If the comparison is verifiably controlled, the work is significant for demonstrating that general-purpose time-series representations can transfer as frozen extractors to EEG foundation models. This has potential to reduce reliance on domain-specific pretraining and to highlight task-dependent needs for temporal modeling complexity. The empirical, controlled design is a positive feature.

major comments (2)

[Abstract] Abstract: The abstract asserts that 'results reveal different trends across the evaluated benchmarks' and that the TSFM 'serves as an effective temporal feature extractor' but supplies no quantitative metrics, error bars, dataset sizes, subject counts, or statistical tests. This prevents assessment of the magnitude or reliability of the claimed performance differences and transferability result.
[Methods] Methods (unified setup description): The central claim that the frozen MOMENT TSFM is an effective transferable extractor rests on the three strategies being compared inside an otherwise identical EEG foundation model. The manuscript must explicitly confirm that TSFM integration introduces no extra steps (input resampling to match pretraining rate, channel-wise projection to expected dimensionality, or special patch-based tokenization handling) absent from the linear and convolutional baselines; without this, any performance edge could be an artifact of those adaptations rather than intrinsic representation quality.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important areas for improving clarity and explicitness. We address each major comment below.

read point-by-point responses

Referee: [Abstract] Abstract: The abstract asserts that 'results reveal different trends across the evaluated benchmarks' and that the TSFM 'serves as an effective temporal feature extractor' but supplies no quantitative metrics, error bars, dataset sizes, subject counts, or statistical tests. This prevents assessment of the magnitude or reliability of the claimed performance differences and transferability result.

Authors: We agree that the abstract would benefit from greater specificity to allow readers to evaluate the results. In the revised version, we will incorporate key quantitative metrics (e.g., mean accuracies or F1 scores with standard deviations across runs or subjects), brief dataset characteristics (subject counts and sample sizes), and reference to any statistical comparisons performed. revision: yes
Referee: [Methods] Methods (unified setup description): The central claim that the frozen MOMENT TSFM is an effective transferable extractor rests on the three strategies being compared inside an otherwise identical EEG foundation model. The manuscript must explicitly confirm that TSFM integration introduces no extra steps (input resampling to match pretraining rate, channel-wise projection to expected dimensionality, or special patch-based tokenization handling) absent from the linear and convolutional baselines; without this, any performance edge could be an artifact of those adaptations rather than intrinsic representation quality.

Authors: The comparison was designed to be controlled, with all three extractors operating on identically preprocessed EEG inputs within the same downstream model. No differential resampling, channel projections, or tokenization steps were applied to the TSFM. To make this fully explicit, we will add a dedicated paragraph (or subsection) in the Methods section describing the shared input pipeline and confirming equivalence of integration steps across the linear, convolutional, and TSFM extractors. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical comparison without derivation or self-referential fitting

full rationale

The paper conducts a controlled empirical comparison of three temporal feature extraction strategies (linear, convolutional, frozen MOMENT TSFM) inside a unified EEG foundation model setup, evaluating them on motor imagery and emotion recognition tasks. No mathematical derivations, equations, parameter fitting presented as predictions, or self-citation chains appear in the abstract or described content. The central claim—that the pretrained TSFM serves as an effective transferable extractor—rests on experimental outcomes rather than any reduction to inputs by construction. This is a standard self-contained empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review based on abstract only; no free parameters, invented entities or ad-hoc axioms are identifiable from the provided text. One domain assumption noted below.

axioms (1)

domain assumption The motor imagery and emotion recognition tasks are representative benchmarks for evaluating EEG representation quality.
The paper uses performance on these two tasks to draw conclusions about the temporal extractors.

pith-pipeline@v0.9.1-grok · 5713 in / 1056 out tokens · 35860 ms · 2026-06-30T05:54:44.760759+00:00 · methodology

discussion (0)

Reference graph

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6 Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model A

accepted for publication. 6 Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model A. Appendix The Appendix provides supplementary details on visualization, model hyperparameters, dataset descriptions, data prepro- cessing, data splits, evaluation metrics, and computational environment. A.1. ...

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The recordings were acquired using an EGI HydroCel Geodesic Sensor Net with 128 channels at a sampling rate of 500 Hz

is a large-scale collection of EEG recordings from children and adolescents aged 5 to 21 years. The recordings were acquired using an EGI HydroCel Geodesic Sensor Net with 128 channels at a sampling rate of 500 Hz. Each subject completed active and passive tasks, including resting state, surround suppression, movie watching, contrast change detection, seq...

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As the data is already band-pass filtered, no additional notch filtering (60 Hz) was applied

after applying average referencing to the remaining channels. As the data is already band-pass filtered, no additional notch filtering (60 Hz) was applied. Downstream Datasets Physio-Net MI.The PhysioNet EEG Motor Movement Dataset (Physio-Net MI) (Goldberger et al., 2000; Schalk, 2009; Schalk et al.,

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It computes the average recall across all classes

Table 3.Summary of Datasets Dataset #subjects #channels length of EEG #samples (Train-Val) or (Train-Val-Test) HBN-EEG (pretrain) 2449 128 10 seconds 482593-64991 PhysioNet-MI 109 64 4 seconds 6629-1429-1347 FACED 123 32 10 seconds 7308-1512-1512 Downstream Evaluation Metrics Balanced Accuracyis a performance metric used to evaluate models on imbalanced d...

2000

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Ansari, A

doi: 10.1038/sdata.2017.181. Ansari, A. F., Stella, L., Turkmen, A. C., Zhang, X., Mer- cado, P., Shen, H., Shchur, O., Rangapuram, S. S., Arango, S. P., Kapoor, S., Zschiegner, J., Maddix, D. C., Wang, H., Mahoney, M. W., Torkkola, K., Wilson, A. G., Bohlke-Schneider, M., and Wang, B. Chronos: Learning the language of time series.Transactions on Machine ...

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work page doi:10.3389/fninf.2015.00016 2015

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Das, A., Kong, W., Sen, R., and Zhou, Y

doi: 10.1038/s41597-023-02650-w. Das, A., Kong, W., Sen, R., and Zhou, Y . A decoder-only foundation model for time-series forecasting. InProceed- ings of the 41st International Conference on Machine Learning, volume 235 ofProceedings of Machine Learn- ing Research, pp. 10148–10167. PMLR, 21–27 Jul

work page doi:10.1038/s41597-023-02650-w

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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N. An image is worth 16x16 words: Transformers for image recognition at scale.ArXiv, abs/2010.11929,

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A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., Parkkonen, L., and H¨am¨al¨ainen, M

Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., Parkkonen, L., and H¨am¨al¨ainen, M. Meg and eeg data analysis with mne-python.Frontiers in Neuroscience, V olume 7 - 2013,

2013

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doi: 10.3389/ fnins.2013.00267

ISSN 1662-453X. doi: 10.3389/ fnins.2013.00267. Jiang, W., Zhao, L., and liang Lu, B. Large brain model for learning generic representations with tremendous EEG data in BCI. InThe Twelfth International Conference on Learning Representations,

work page arXiv 2013

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Bendr: Us- ing transformers and a contrastive self-supervised learn- ing task to learn from massive amounts of eeg data.Fron- tiers in Human Neuroscience, V olume 15 - 2021,

Kostas, D., Aroca-Ouellette, S., and Rudzicz, F. Bendr: Us- ing transformers and a contrastive self-supervised learn- ing task to learn from massive amounts of eeg data.Fron- tiers in Human Neuroscience, V olume 15 - 2021,

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doi: 10.3389/fnhum.2021.653659

ISSN 1662-5161. doi: 10.3389/fnhum.2021.653659. Kottapalli, S., Hubli, K., Chandrashekhara, S., Jain, G., Hubli, S., Botla, G., and Doddaiah, R. Foundation models for time series: A survey.ArXiv, abs/2504.04011,

work page doi:10.3389/fnhum.2021.653659 2021

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Q., Ibrahim, H., Abdullah, M

5 Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model Lai, C. Q., Ibrahim, H., Abdullah, M. Z., Abdullah, J. M., Suandi, S. A., and Azman, A. Artifacts and noise removal for electroencephalogram (eeg): A literature review. In 2018 IEEE Symposium on Computer Applications & In- dustrial Elec...

2018

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A simple review of eeg foundation models: Datasets, advancements and future perspectives.arXiv preprint arXiv:2504.20069,

Lai, J., Wei, J., Yao, L., and Wang, Y . A simple review of eeg foundation models: Datasets, advancements and future perspectives.arXiv preprint arXiv:2504.20069,

work page arXiv

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Ouahidi, Y

doi: 10.1038/sdata.2017.40. Ouahidi, Y . E., Lys, J., Th¨olke, P., Farrugia, N., Pasdeloup, B., Gripon, V ., Jerbi, K., and Lioi, G. Reve: A foundation model for eeg – adapting to any setup with large-scale pretraining on 25,000 subjects

work page doi:10.1038/sdata.2017.40 2017

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doi: 10.1016/0013-4694(89)90180-6

ISSN 0013-4694. doi: 10.1016/0013-4694(89)90180-6. Schalk, G. EEG Motor Movement/Imagery Dataset.Phys- ioNet, September

work page doi:10.1016/0013-4694(89)90180-6

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Version 1.0.0

doi: 10.13026/C28G6P. Version 1.0.0. Schalk, G., McFarland, D., Hinterberger, T., Birbaumer, N., and Wolpaw, J. Bci2000: a general-purpose brain- computer interface (bci) system.IEEE Transactions on Biomedical Engineering, 51(6):1034–1043,

work page doi:10.13026/c28g6p

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Shirazi, S

doi: 10.1109/TBME.2004.827072. Shirazi, S. Y ., Franco, A., Hoffmann, M. S., Esper, N. B., Truong, D., Delorme, A., Milham, M. P., and Makeig, S. Hbn-eeg: The fair implementation of the healthy brain network (hbn) electroencephalography dataset.bioRxiv,

work page doi:10.1109/tbme.2004.827072 2004

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Song, Y ., Zheng, Q., Liu, B., and Gao, X

doi: 10.1101/2024.10.03.615261. Song, Y ., Zheng, Q., Liu, B., and Gao, X. Eeg conformer: Convolutional transformer for eeg decoding and visu- alization.IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31:710–719,

work page doi:10.1101/2024.10.03.615261 2024

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Tran, X.-T., V o, T.-N., Vu, S.-T., Tran, T.-T., Nguyen, M.-D., Do, T., and Lin, C.-T

doi: 10.1109/TNSRE.2022.3230250. Tran, X.-T., V o, T.-N., Vu, S.-T., Tran, T.-T., Nguyen, M.-D., Do, T., and Lin, C.-T. Inter-and intra-subject variability in eeg: A systematic survey.arXiv preprint arXiv:2602.01019,

work page doi:10.1109/tnsre.2022.3230250 2022

[20] [20]

6 Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model A

accepted for publication. 6 Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model A. Appendix The Appendix provides supplementary details on visualization, model hyperparameters, dataset descriptions, data prepro- cessing, data splits, evaluation metrics, and computational environment. A.1. ...

2024

[21] [21]

The recordings were acquired using an EGI HydroCel Geodesic Sensor Net with 128 channels at a sampling rate of 500 Hz

is a large-scale collection of EEG recordings from children and adolescents aged 5 to 21 years. The recordings were acquired using an EGI HydroCel Geodesic Sensor Net with 128 channels at a sampling rate of 500 Hz. Each subject completed active and passive tasks, including resting state, surround suppression, movie watching, contrast change detection, seq...

2013

[22] [22]

As the data is already band-pass filtered, no additional notch filtering (60 Hz) was applied

after applying average referencing to the remaining channels. As the data is already band-pass filtered, no additional notch filtering (60 Hz) was applied. Downstream Datasets Physio-Net MI.The PhysioNet EEG Motor Movement Dataset (Physio-Net MI) (Goldberger et al., 2000; Schalk, 2009; Schalk et al.,

2000

[23] [23]

It computes the average recall across all classes

Table 3.Summary of Datasets Dataset #subjects #channels length of EEG #samples (Train-Val) or (Train-Val-Test) HBN-EEG (pretrain) 2449 128 10 seconds 482593-64991 PhysioNet-MI 109 64 4 seconds 6629-1429-1347 FACED 123 32 10 seconds 7308-1512-1512 Downstream Evaluation Metrics Balanced Accuracyis a performance metric used to evaluate models on imbalanced d...

2000