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Paper Citation Record · LEDGER

Decoding speech perception from non-invasive brain recordings

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2208.12266.

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

pith.paper-citation-record.v1
2208.12266 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:23:37.026830Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T02:49:25.391649Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2d3e52a5-a790-474d-8283-d775d4bbf6a8 · inbound

Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings cites this paper.

Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings Decoding speech perception from non-invasive brain recordings

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-11T11:23:37.026830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:23:37.026830Z digest=sha256:8662f86b56393a5ec641d5e63012f6d38fb364a2da5f4ff29f4ec95f8bbcc302

Observation 047cd3ec-99eb-43f9-87c7-ce5f43ff6db2 · inbound

Bridging Auditory Perception and Language Comprehension through MEG-Driven Encoding Models cites this paper.

Bridging Auditory Perception and Language Comprehension through MEG-Driven Encoding Models Decoding speech perception from non-invasive brain recordings

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T05:49:00.588216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:49:00.588216Z digest=sha256:c49cf1debae3d719cdb93c45eddef1e65a47aa3ae38decb1c2265411b5b8a720

Observation 4ece071d-697b-4b41-b5ed-721a5a445615 · inbound

Scaling laws for decoding images from brain activity cites this paper.

Scaling laws for decoding images from brain activity Decoding speech perception from non-invasive brain recordings

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-10T14:28:37.011570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:28:37.011570Z digest=sha256:4cceec46eb0a8a0f1fcff9f1ed9c234afdd538d7d574f215db359bd2443b7f51

Observation 9f942eed-5fed-4c43-97d2-366cb839f4a7 · inbound

Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants cites this paper.

Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants Decoding speech perception from non-invasive brain recordings

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T13:40:18.640382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:40:18.640382Z digest=sha256:d14d8d497d3a1facbc974b2050fd7489f48693b19c11313f2f98acb9c7e11a85

Observation eb09cb0c-f9e4-4089-a037-13256eb6bb10 · inbound

Neuro2Semantic: A Transfer Learning Framework for Semantic Reconstruction of Continuous Language from Human Intracranial EEG cites this paper.

Neuro2Semantic: A Transfer Learning Framework for Semantic Reconstruction of Continuous Language from Human Intracranial EEG Decoding speech perception from non-invasive brain recordings

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T12:11:11.607930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:11:11.607930Z digest=sha256:5d3d1fc92abb4d8ae68aa2e8ab9756688637d0dbfc311a784fe15e76a6b2837c

Observation 598e9584-1235-43a8-afdc-a5936954bb03 · inbound

NeuralBench: A Unifying Framework to Benchmark NeuroAI Models cites this paper.

NeuralBench: A Unifying Framework to Benchmark NeuroAI Models Decoding speech perception from non-invasive brain recordings

Reference 249

Resolution
verified exact
arxiv_id, observed 2026-05-12T02:16:15.686175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-05-12T02:16:10.680353Z digest=sha256:3b4c55eee8ac2a4ba32c6b8203baa1e2b86c556226439111ab1fea89c16be499

Observation c7d1d2b8-4bda-4d8d-aadf-2ffb81e316f5 · inbound

Can neurons speak? Semantic narration of vision at single-cell resolution cites this paper.

Can neurons speak? Semantic narration of vision at single-cell resolution Decoding speech perception from non-invasive brain recordings

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-07-04T02:49:25.393207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-26T18:52:22.357765Z digest=sha256:d5c36a9ef4766ec25036914cdafb0842fdb7746439fd195d157948d40a4d2a24

Observation 1a8f79fa-a91b-4549-9018-5166a4c5f002 · inbound

Toward Annotation-Efficient Continuous Emotion Arousal Quantification via Group-Level EEG Dynamic Neural Synchrony cites this paper.

Toward Annotation-Efficient Continuous Emotion Arousal Quantification via Group-Level EEG Dynamic Neural Synchrony Decoding speech perception from non-invasive brain recordings

Reference 111

Resolution
unresolved
no resolver link, observed 2026-07-31T14:47:01.843480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T14:47:01.843480Z digest=sha256:e13c5440fd30c8c82d3aea01d283207a1533f17e71816328123571910f5d4bc5