Bridging Brain with Foundation Models through Self-Supervised Learning | Paper Citation Record

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This paper cites A Cookbook of Self-Supervised Learning.

Bridging Brain with Foundation Models through Self-Supervised Learning A Cookbook of Self-Supervised Learning

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This paper cites Extracting and composing robust features with denoising autoencoders,.

Bridging Brain with Foundation Models through Self-Supervised Learning Extracting and composing robust features with denoising autoencoders,

Reference 2

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This paper cites Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.,.

Bridging Brain with Foundation Models through Self-Supervised Learning Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.,

Reference 3

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This paper cites Efficient Estimation of Word Representations in Vector Space.

Bridging Brain with Foundation Models through Self-Supervised Learning Efficient Estimation of Word Representations in Vector Space

Reference 4

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This paper cites Attention is all you need,.

Bridging Brain with Foundation Models through Self-Supervised Learning Attention is all you need,

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This paper cites Dynamic convolution with multilevel attention for EEG -based motor imagery decoding,.

Bridging Brain with Foundation Models through Self-Supervised Learning Dynamic convolution with multilevel attention for EEG -based motor imagery decoding,

Reference 6

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This paper cites Attention -Inception and Long Short -Term Memory - based Electroencephalography Classification for Motor Imagery Tasks in Rehabilitation,.

Bridging Brain with Foundation Models through Self-Supervised Learning Attention -Inception and Long Short -Term Memory - based Electroencephalography Classification for Motor Imagery Tasks in Rehabilitation,

Reference 7

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This paper cites A Multi -Branch Convolutional Neural Network with Squeeze - and-Excitation Attention Blocks for EEG -Based Motor Imagery Signals Classification,.

Bridging Brain with Foundation Models through Self-Supervised Learning A Multi -Branch Convolutional Neural Network with Squeeze - and-Excitation Attention Blocks for EEG -Based Motor Imagery Signals Classification,

Reference 8

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Observation 60f6cfe0-6d38-4423-a2d7-410dca64d3ad · outbound

This paper cites Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review,.

Bridging Brain with Foundation Models through Self-Supervised Learning Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review,

Reference 9

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This paper cites Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI,.

Bridging Brain with Foundation Models through Self-Supervised Learning Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI,

Reference 10

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This paper cites Combining brain–computer interfaces and assistive technologies: state -of-the-art and challenges,.

Bridging Brain with Foundation Models through Self-Supervised Learning Combining brain–computer interfaces and assistive technologies: state -of-the-art and challenges,

Reference 11

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This paper cites an unresolved cited work.

Bridging Brain with Foundation Models through Self-Supervised Learning Unresolved cited work

Reference 12

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Observation 8b06cff0-500f-4f5e-bf8d-e10e7a2721a6 · outbound

This paper cites Signal quality of simultaneously recorded invasive and non-invasive EEG,.

Bridging Brain with Foundation Models through Self-Supervised Learning Signal quality of simultaneously recorded invasive and non-invasive EEG,

Reference 13

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This paper cites an unresolved cited work.

Bridging Brain with Foundation Models through Self-Supervised Learning Unresolved cited work

Reference 14

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Observation 46dada7c-a1de-4208-b06e-947406b56092 · outbound

This paper cites CHB-MIT Scalp EEG Database.

Bridging Brain with Foundation Models through Self-Supervised Learning CHB-MIT Scalp EEG Database

Reference 15

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This paper cites Masked autoencoders are scalable vision learners,.

Bridging Brain with Foundation Models through Self-Supervised Learning Masked autoencoders are scalable vision learners,

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This paper cites On the Opportunities and Risks of Foundation Models.

Bridging Brain with Foundation Models through Self-Supervised Learning On the Opportunities and Risks of Foundation Models

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This paper cites Brain -conditional multimodal synthesis: A survey and taxonomy,.

Bridging Brain with Foundation Models through Self-Supervised Learning Brain -conditional multimodal synthesis: A survey and taxonomy,

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This paper cites Preferred reporting items for systematic reviews and meta - analyses: the PRISMA statement,.

Bridging Brain with Foundation Models through Self-Supervised Learning Preferred reporting items for systematic reviews and meta - analyses: the PRISMA statement,

Reference 19

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This paper cites Self-taught learning: transfer learning from unlabeled data,.

Bridging Brain with Foundation Models through Self-Supervised Learning Self-taught learning: transfer learning from unlabeled data,

Reference 20

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Observation 732c5e3b-8c3c-4b6b-8919-e4af4cdbe075 · outbound

This paper cites Auto-Encoding Variational Bayes.

Bridging Brain with Foundation Models through Self-Supervised Learning Auto-Encoding Variational Bayes

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This paper cites Glove: Global vectors for word representation,.

Bridging Brain with Foundation Models through Self-Supervised Learning Glove: Global vectors for word representation,

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This paper cites Unsupervised visual representation learning by context prediction,.

Bridging Brain with Foundation Models through Self-Supervised Learning Unsupervised visual representation learning by context prediction,

Reference 23

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This paper cites Colorful image colorization,.

Bridging Brain with Foundation Models through Self-Supervised Learning Colorful image colorization,

Reference 24

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This paper cites Unsupervised learning of visual representations by solving jigsaw puzzles,.

Bridging Brain with Foundation Models through Self-Supervised Learning Unsupervised learning of visual representations by solving jigsaw puzzles,

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This paper cites Context encoders: Feature learning by inpainting,.

Bridging Brain with Foundation Models through Self-Supervised Learning Context encoders: Feature learning by inpainting,

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This paper cites Unsupervised Representation Learning by Predicting Image Rotations.

Bridging Brain with Foundation Models through Self-Supervised Learning Unsupervised Representation Learning by Predicting Image Rotations

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This paper cites Momentum contrast for unsupervised visual representation learning,.

Bridging Brain with Foundation Models through Self-Supervised Learning Momentum contrast for unsupervised visual representation learning,

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This paper cites A simple framework for contrastive learning of visual representations,.

Bridging Brain with Foundation Models through Self-Supervised Learning A simple framework for contrastive learning of visual representations,

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This paper cites Bootstrap your own latent -a new approach to self-supervised learning,.

Bridging Brain with Foundation Models through Self-Supervised Learning Bootstrap your own latent -a new approach to self-supervised learning,

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This paper cites Bert: Pre - training of deep bidirectional transformers for language understanding,.

Bridging Brain with Foundation Models through Self-Supervised Learning Bert: Pre - training of deep bidirectional transformers for language understanding,

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This paper cites Improving language understanding by generative pre -training,.

Bridging Brain with Foundation Models through Self-Supervised Learning Improving language understanding by generative pre -training,

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This paper cites Language models are unsupervised multitask learners,.

Bridging Brain with Foundation Models through Self-Supervised Learning Language models are unsupervised multitask learners,

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This paper cites Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks,.

Bridging Brain with Foundation Models through Self-Supervised Learning Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks,

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This paper cites Deap: A database for emotion analysis; using physiological signals,.

Bridging Brain with Foundation Models through Self-Supervised Learning Deap: A database for emotion analysis; using physiological signals,

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This paper cites A modality -independent proto -organization of human multisensory areas,.

Bridging Brain with Foundation Models through Self-Supervised Learning A modality -independent proto -organization of human multisensory areas,

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This paper cites Brain-JEPA: Brain dynamics foundation model with gradient positioning and spatiotemporal masking,.

Bridging Brain with Foundation Models through Self-Supervised Learning Brain-JEPA: Brain dynamics foundation model with gradient positioning and spatiotemporal masking,

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This paper cites Brant: Foundation Model for Intracranial Neural Signal,.

Bridging Brain with Foundation Models through Self-Supervised Learning Brant: Foundation Model for Intracranial Neural Signal,

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This paper cites CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding,.

Bridging Brain with Foundation Models through Self-Supervised Learning CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding,

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This paper cites Eegpt: Pretrained transformer for universal and reliable representation of eeg signals,.

Bridging Brain with Foundation Models through Self-Supervised Learning Eegpt: Pretrained transformer for universal and reliable representation of eeg signals,

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This paper cites Simmim: A simple framework for masked image modeling,.

Bridging Brain with Foundation Models through Self-Supervised Learning Simmim: A simple framework for masked image modeling,

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This paper cites EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model,.

Bridging Brain with Foundation Models through Self-Supervised Learning EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model,

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This paper cites GEFM: Graph - Enhanced EEG Foundation Model,.

Bridging Brain with Foundation Models through Self-Supervised Learning GEFM: Graph - Enhanced EEG Foundation Model,

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This paper cites NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals,.

Bridging Brain with Foundation Models through Self-Supervised Learning NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals,

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This paper cites Promoting Cross -Modal Representations to Improve Multimodal Foundation Models for Physiological Signals,.

Bridging Brain with Foundation Models through Self-Supervised Learning Promoting Cross -Modal Representations to Improve Multimodal Foundation Models for Physiological Signals,

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This paper cites Knowledge-guided EEG Representation Learning,.

Bridging Brain with Foundation Models through Self-Supervised Learning Knowledge-guided EEG Representation Learning,

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This paper cites Neuro-GPT: Towards A Foundation Model For EEG,.

Bridging Brain with Foundation Models through Self-Supervised Learning Neuro-GPT: Towards A Foundation Model For EEG,

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This paper cites BrainGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training.

Bridging Brain with Foundation Models through Self-Supervised Learning BrainGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training

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This paper cites Enhancing EEG -to-Text Decoding through Transferable Representations from Pre -trained Contrastive EEG -Text Masked Autoencoder,.

Bridging Brain with Foundation Models through Self-Supervised Learning Enhancing EEG -to-Text Decoding through Transferable Representations from Pre -trained Contrastive EEG -Text Masked Autoencoder,

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This paper cites Neuro-BERT: Rethinking Masked Autoencoding for Self -Supervised Neurological Pretraining,.

Bridging Brain with Foundation Models through Self-Supervised Learning Neuro-BERT: Rethinking Masked Autoencoding for Self -Supervised Neurological Pretraining,

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This paper cites Brainbert: Self -Supervised Representation Learning for Intracranial Recordings,.

Bridging Brain with Foundation Models through Self-Supervised Learning Brainbert: Self -Supervised Representation Learning for Intracranial Recordings,

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This paper cites Representation Learning with Contrastive Predictive Coding.

Bridging Brain with Foundation Models through Self-Supervised Learning Representation Learning with Contrastive Predictive Coding

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This paper cites Exploring simple siamese representation learning,.

Bridging Brain with Foundation Models through Self-Supervised Learning Exploring simple siamese representation learning,

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This paper cites Emerging properties in self -supervised vision transformers,.

Bridging Brain with Foundation Models through Self-Supervised Learning Emerging properties in self -supervised vision transformers,

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This paper cites Barlow twins: Self -supervised learning via redundancy reduction,.

Bridging Brain with Foundation Models through Self-Supervised Learning Barlow twins: Self -supervised learning via redundancy reduction,

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This paper cites VICReg: Variance - Invariance-Covariance Regularization For Self -Supervised Learning,.

Bridging Brain with Foundation Models through Self-Supervised Learning VICReg: Variance - Invariance-Covariance Regularization For Self -Supervised Learning,

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This paper cites MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals,.

Bridging Brain with Foundation Models through Self-Supervised Learning MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals,

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This paper cites BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data,.

Bridging Brain with Foundation Models through Self-Supervised Learning BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data,

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This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representations,.

Bridging Brain with Foundation Models through Self-Supervised Learning wav2vec 2.0: A framework for self-supervised learning of speech representations,

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This paper cites Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition,.

Bridging Brain with Foundation Models through Self-Supervised Learning Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition,

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Observation ff384e6b-75e6-4588-be9d-1d7432e0c088 · outbound

This paper cites Biot: Biosignal transformer for cross-data learning in the wild,.

Bridging Brain with Foundation Models through Self-Supervised Learning Biot: Biosignal transformer for cross-data learning in the wild,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:34.975255Z

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Observation a692d996-5803-460c-8a6e-1c282d684960 · outbound

This paper cites EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping.

Bridging Brain with Foundation Models through Self-Supervised Learning EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:47:43.831825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation be606bb1-0e73-423f-ac2a-77239070bfbd · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Bridging Brain with Foundation Models through Self-Supervised Learning Learning transferable visual models from natural language supervision,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:35.084127Z

Source-reported events for the cited work

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Observation 41ba384c-a049-4610-a21c-093d0006747c · outbound

This paper cites VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding.

Bridging Brain with Foundation Models through Self-Supervised Learning VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:35.139511Z

Source-reported events for the cited work

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Observation 818ff535-9bf6-4877-a372-35c7cb29a25b · outbound

This paper cites Active Contrastive Learning of Audio-Visual Video Representations.

Bridging Brain with Foundation Models through Self-Supervised Learning Active Contrastive Learning of Audio-Visual Video Representations

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:35.237201Z

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Unavailable: canonical work link unavailable.

Observation d1415bc9-8fb4-4770-82b2-ad12e0fb66f7 · outbound

This paper cites Cross-modal Contrastive Learning for Speech Translation.

Bridging Brain with Foundation Models through Self-Supervised Learning Cross-modal Contrastive Learning for Speech Translation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:35.289623Z

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Unavailable: canonical work link unavailable.

Observation 684d74eb-1b2a-4e52-a114-e2eb0eed40eb · outbound

This paper cites Multi -modal cross -domain self -supervised pre - training for fMRI and EEG fusion,.

Bridging Brain with Foundation Models through Self-Supervised Learning Multi -modal cross -domain self -supervised pre - training for fMRI and EEG fusion,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:35.348039Z

Source-reported events for the cited work

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Observation c9bdd251-3481-42dd-accc-268e4a7b06c9 · outbound

This paper cites Brant -X: A Unified Physiological Signal Alignment Framework,.

Bridging Brain with Foundation Models through Self-Supervised Learning Brant -X: A Unified Physiological Signal Alignment Framework,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:35.435623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf722c35-03a1-4a9b-bc34-c906f8b2341a · outbound

This paper cites Self -supervised cross-modal visual retrieval from brain activities,.

Bridging Brain with Foundation Models through Self-Supervised Learning Self -supervised cross-modal visual retrieval from brain activities,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:35.505095Z

Source-reported events for the cited work

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Observation 38bdb776-ae2e-49fd-84f3-fb30935b25db · outbound

This paper cites BELT-2: Bootstrapping EEG-to-Language representation alignment for multi-task brain decoding.

Bridging Brain with Foundation Models through Self-Supervised Learning BELT-2: Bootstrapping EEG-to-Language representation alignment for multi-task brain decoding

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:35.582368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b8e1d9f-21e5-4d2b-b7c3-6856eb1ed255 · outbound

This paper cites Optimising EEG decoding with refined sampling and multimodal feature integration.

Bridging Brain with Foundation Models through Self-Supervised Learning Optimising EEG decoding with refined sampling and multimodal feature integration

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:47:43.517572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bd48cd8e-2594-42c6-99d3-82d1fdd88b46 · outbound

This paper cites Decoding Natural Images from EEG for Object Recognition,.

Bridging Brain with Foundation Models through Self-Supervised Learning Decoding Natural Images from EEG for Object Recognition,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:35.704334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce2a0890-d024-4e84-bed9-e1c1b2c043ad · outbound

This paper cites NeuroBind: Towards Unified Multimodal Representations for Neural Signals,.

Bridging Brain with Foundation Models through Self-Supervised Learning NeuroBind: Towards Unified Multimodal Representations for Neural Signals,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:35.767890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86a98885-0df9-49bc-b1ce-74bff5d204fd · outbound

This paper cites A Knowledge-Driven Cross -view Contrastive Learning for EEG Representation,.

Bridging Brain with Foundation Models through Self-Supervised Learning A Knowledge-Driven Cross -view Contrastive Learning for EEG Representation,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:35.858206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fa05ff2-ac23-4c98-b44f-a1b4838c3936 · outbound

This paper cites Uncovering the structure of clinical EEG signals with self-supervised learning,.

Bridging Brain with Foundation Models through Self-Supervised Learning Uncovering the structure of clinical EEG signals with self-supervised learning,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:35.949494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40f417fb-6f10-4452-9894-47d93901ae7e · outbound

This paper cites Toward Generalizing Visual Brain Decoding to Unseen Subjects,.

Bridging Brain with Foundation Models through Self-Supervised Learning Toward Generalizing Visual Brain Decoding to Unseen Subjects,

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:36.042873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a69d1cc9-ae65-4905-9d2b-4856b40a60db · outbound

This paper cites Language models are few -shot learners,.

Bridging Brain with Foundation Models through Self-Supervised Learning Language models are few -shot learners,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:36.109563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b3bca7a-3dea-43bd-9eec-431769c8913b · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Bridging Brain with Foundation Models through Self-Supervised Learning Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:53.385490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 398ea0a8-aeeb-40f5-b101-1e9ba0278fda · outbound

This paper cites Brant -2: Foundation model for brain signals,.

Bridging Brain with Foundation Models through Self-Supervised Learning Brant -2: Foundation model for brain signals,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:53.216721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 03ebe1b1-ec4a-47dd-bf9c-409f2803efb4 · outbound

This paper cites BrainWave: A Brain Signal Foundation Model for Clinical Applications.

Bridging Brain with Foundation Models through Self-Supervised Learning BrainWave: A Brain Signal Foundation Model for Clinical Applications

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:36.438638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b23a63f9-9810-4f31-8a17-dc709eb1bf01 · outbound

This paper cites {MAEEG}: Masked Auto -encoder for {EEG} Representation Learning,.

Bridging Brain with Foundation Models through Self-Supervised Learning {MAEEG}: Masked Auto -encoder for {EEG} Representation Learning,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:53.057494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a4bcf928-34b4-437e-b4a1-53f08898fe26 · outbound

This paper cites Anatomical Foundation Models for Brain MRIs,.

Bridging Brain with Foundation Models through Self-Supervised Learning Anatomical Foundation Models for Brain MRIs,

Reference 82

Resolution: verified exact; raw_fallback, observed 2026-08-06T23:47:43.332562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a74b061d-4fdb-4697-b3d1-69395dc3f16c · outbound

This paper cites BrainLM: A foundation model for brain activity recordings,.

Bridging Brain with Foundation Models through Self-Supervised Learning BrainLM: A foundation model for brain activity recordings,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:52.832737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0021c995-d0be-44ae-88a4-d070f36c5f5c · outbound

This paper cites BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signals.

Bridging Brain with Foundation Models through Self-Supervised Learning BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signals

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:37.101822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ce8799a-9d14-441c-a582-d4e97c7a71c7 · outbound

This paper cites BrainSegFounder: towards 3D foundation models for neuroimage segmentation,.

Bridging Brain with Foundation Models through Self-Supervised Learning BrainSegFounder: towards 3D foundation models for neuroimage segmentation,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:52.660788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4934efc8-a994-40ad-b57e-0b3c1063733d · outbound

This paper cites CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention,.

Bridging Brain with Foundation Models through Self-Supervised Learning CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention,

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:37.292022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7128af5d-a7d2-4cbc-af14-135a54bdc14b · outbound

This paper cites FM -APP: Foundation Model for Any Phenotype Prediction via fMRI to sMRI Knowledge Transfer,.

Bridging Brain with Foundation Models through Self-Supervised Learning FM -APP: Foundation Model for Any Phenotype Prediction via fMRI to sMRI Knowledge Transfer,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:52.459355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 194e0df7-4f36-4627-bc6f-ba34d16f1547 · outbound

This paper cites An Approach to Building Foundation Models for Brain Image Analysis,.

Bridging Brain with Foundation Models through Self-Supervised Learning An Approach to Building Foundation Models for Brain Image Analysis,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:52.277948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 51152ebd-e6c3-4d59-81c7-f44075523290 · outbound

This paper cites FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling.

Bridging Brain with Foundation Models through Self-Supervised Learning FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:37.669828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5b9d6ec-263f-447f-83d7-40401b789552 · outbound

This paper cites Large Cognition Model: Towards Pretrained EEG Foundation Model.

Bridging Brain with Foundation Models through Self-Supervised Learning Large Cognition Model: Towards Pretrained EEG Foundation Model

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T23:47:37.806693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b42582d2-5eec-4d5e-85f2-23bc70c126a3 · outbound

This paper cites MEET: A Multi-Band EEG Transformer for Brain States Decoding,.

Bridging Brain with Foundation Models through Self-Supervised Learning MEET: A Multi-Band EEG Transformer for Brain States Decoding,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:51.995280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 047cd7d8-0ce4-43ee-8689-608b037c2d2a · outbound

This paper cites Meta Transfer of Self -Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction,.

Bridging Brain with Foundation Models through Self-Supervised Learning Meta Transfer of Self -Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:51.800733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation feaa8d43-e08e-4b9d-9c34-0e5b2a50d4d6 · outbound

This paper cites Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks.

Bridging Brain with Foundation Models through Self-Supervised Learning Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks

Reference 93

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:47:42.983793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 39411b6f-f8fd-4d37-8ac9-13ea55a9c199 · outbound

This paper cites Self - supervised contrastive pre -training for time series via time - frequency consistency,.

Bridging Brain with Foundation Models through Self-Supervised Learning Self - supervised contrastive pre -training for time series via time - frequency consistency,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:51.562154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c43332b0-3034-4364-8d31-f3061a6ee7c9 · outbound

This paper cites Physics - Informed Attention Temporal Convolutional Network for EEG - Based Motor Imagery Classification,.

Bridging Brain with Foundation Models through Self-Supervised Learning Physics - Informed Attention Temporal Convolutional Network for EEG - Based Motor Imagery Classification,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:51.359171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9f6383f6-0a96-4242-b820-c46e56d919c0 · outbound

This paper cites Attention based Inception model for robust EEG motor imagery classification,.

Bridging Brain with Foundation Models through Self-Supervised Learning Attention based Inception model for robust EEG motor imagery classification,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:51.120371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f8e8c7c5-51c0-4f93-9cdc-4f148524630a · outbound

This paper cites MOABB: trustworthy algorithm benchmarking for BCIs,.

Bridging Brain with Foundation Models through Self-Supervised Learning MOABB: trustworthy algorithm benchmarking for BCIs,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:50.983133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cdeacc5b-b1ad-43d7-8737-ba640b39d276 · outbound

This paper cites REH-MI: EEG Motor Imagery Dataset from the Same Limb for Rehabilitation Applications.

Bridging Brain with Foundation Models through Self-Supervised Learning REH-MI: EEG Motor Imagery Dataset from the Same Limb for Rehabilitation Applications

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:50.807336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 494692ee-4a33-43d9-acdb-5d2a84d49146 · outbound

This paper cites Neurobolt: Resting-state eeg-to-fmri synthesis with multi-dimensional feature mapping,.

Bridging Brain with Foundation Models through Self-Supervised Learning Neurobolt: Resting-state eeg-to-fmri synthesis with multi-dimensional feature mapping,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:50.637553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e3ee9252-2e9b-45e7-a39f-e433d6607f40 · outbound

This paper cites CATD: Unified Representation Learning for EEG -To-fMRI Cross-Modal Generation,.

Bridging Brain with Foundation Models through Self-Supervised Learning CATD: Unified Representation Learning for EEG -To-fMRI Cross-Modal Generation,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:47:50.502022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers