Paper Citation Record · LEDGER

Benchmarking Positional Encoding Strategies for Transformer-Based EEG Foundation Models

As of 19 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 0 inbound Pith citation observations for arXiv:2605.29754.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2605.29754 v1

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T07:41:17.484730Z

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

6 of 6 outbound references displayed

verified exact1
verified fuzzy0
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f05a2b05-1230-4095-863f-78b0719bce68 · outbound

This paper cites EEG classification for neurological disorders using frequency band deciles.

Benchmarking Positional Encoding Strategies for Transformer-Based EEG Foundation Models EEG classification for neurological disorders using frequency band deciles

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-29T07:41:17.484730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8c446a7-6a58-4a3f-98c6-6027371c61df · outbound

This paper cites BENDR: Us- ing transformers and a contrastive self-supervised learn- ing task to learn from massive amounts of EEG data.

Benchmarking Positional Encoding Strategies for Transformer-Based EEG Foundation Models BENDR: Us- ing transformers and a contrastive self-supervised learn- ing task to learn from massive amounts of EEG data

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-29T07:41:17.484730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7bb4e70-774d-4870-a8fe-770131f7e581 · outbound

This paper cites REVE: A foundation model for EEG–adapting to any setup with large-scale pretraining on 25,000 subjects.

Benchmarking Positional Encoding Strategies for Transformer-Based EEG Foundation Models REVE: A foundation model for EEG–adapting to any setup with large-scale pretraining on 25,000 subjects

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-29T07:43:13.476582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 598b9b5c-eeeb-4bb9-8af7-56922b38764b · outbound

This paper cites Flexible patched brain transformer model for EEG decoding.

Benchmarking Positional Encoding Strategies for Transformer-Based EEG Foundation Models Flexible patched brain transformer model for EEG decoding

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-29T07:41:17.484730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 755a2c6a-ece1-4746-9127-a9a8b2bb3ef9 · outbound

This paper cites An open resource for transdiagnos- tic research in pediatric mental health and learning disor- ders.

Benchmarking Positional Encoding Strategies for Transformer-Based EEG Foundation Models An open resource for transdiagnos- tic research in pediatric mental health and learning disor- ders

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-29T07:41:17.484730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c8baf35-7986-44d5-83ad-24d81d8b27ab · outbound

This paper cites A large finer-grained affective computing EEG dataset.

Benchmarking Positional Encoding Strategies for Transformer-Based EEG Foundation Models A large finer-grained affective computing EEG dataset

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-29T07:41:17.484730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.