Paper Citation Record · LEDGER

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices

As of 22 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2607.18149.

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

pith.paper-citation-record.v1
2607.18149 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T15:53:45.442161Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

30 of 30 outbound references displayed

verified exact0
verified fuzzy0
unresolved30
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d39b1827-6137-4de7-a758-887eaae71b07 · outbound

This paper cites Deep Learning Approaches for EEG-Motor Imagery-Based BCIs: Current Models, Generalization Challenges, and Emerging Trends.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Deep Learning Approaches for EEG-Motor Imagery-Based BCIs: Current Models, Generalization Challenges, and Emerging Trends

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:42.832292Z

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Unavailable: canonical work link unavailable.

Observation 166e403d-adf4-455e-8fd6-f8641b9eaeed · outbound

This paper cites A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:43.002201Z

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Observation f8983724-a092-43ee-a476-5af4094f8a68 · outbound

This paper cites Challenges, Limitations, and Future Directions in EEG-Driven Image Classification using Machine Learning.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Challenges, Limitations, and Future Directions in EEG-Driven Image Classification using Machine Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:43.063792Z

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Observation ac4953d3-2026-4226-8819-1f0c94406d37 · outbound

This paper cites Eeg founda- tion models: A critical review of current progress and future directions.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Eeg founda- tion models: A critical review of current progress and future directions

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:43.172965Z

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Observation 4958cb36-6e2c-4c99-9567-d7df18dbd119 · outbound

This paper cites A Comprehensive Survey on Wearable Computing for Mental and Physical Health Monitoring.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices A Comprehensive Survey on Wearable Computing for Mental and Physical Health Monitoring

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:43.249710Z

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Unavailable: canonical work link unavailable.

Observation b1c5bb81-161d-4e57-b72a-8706609095a4 · outbound

This paper cites A low-latency neural inference framework for real-time handwriting recogni- tion from EEG signals on an edge device: O. Sen et al.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices A low-latency neural inference framework for real-time handwriting recogni- tion from EEG signals on an edge device: O. Sen et al

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:43.374267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 612a6b68-3c50-4f96-ade3-fe086d705470 · outbound

This paper cites Deep differentiable logic gate networks.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Deep differentiable logic gate networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:43.458370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ffdcd0a-6e2e-4079-bc6f-0d3f42a6e34a · outbound

This paper cites Convolutional differentiable logic gate networks.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Convolutional differentiable logic gate networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:43.536216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef9ec059-0775-4309-8cee-4b91739ee2f8 · outbound

This paper cites Recurrent deep differentiable logic gate networks.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Recurrent deep differentiable logic gate networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:43.627439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a27a6d2-36a8-4ee1-ac74-83a500a41774 · outbound

This paper cites Light Differentiable Logic Gate Networks.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Light Differentiable Logic Gate Networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:43.731320Z

Source-reported events for the cited work

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Observation 15f0ee82-f265-406f-8d07-34526a4964dd · outbound

This paper cites Deep learning with convolutional neural networks for EEG decoding and visualization.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Deep learning with convolutional neural networks for EEG decoding and visualization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:43.810852Z

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Observation 7ae1d7e5-dd14-4eb5-9a0d-ad4a1614a6b1 · outbound

This paper cites EEGNet: a compact convolutional neural network for EEG-based brain–computer inter- faces.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices EEGNet: a compact convolutional neural network for EEG-based brain–computer inter- faces

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:43.881019Z

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Observation e737d1b8-4b94-4a6e-86fc-27f983a25f13 · outbound

This paper cites EEG conformer: Convolutional transformer for EEG decoding and visualization.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices EEG conformer: Convolutional transformer for EEG decoding and visualization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:43.937631Z

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Unavailable: canonical work link unavailable.

Observation 915b3628-c33c-4123-be13-a28f866c1ff1 · outbound

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

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:44.031741Z

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Observation fb5d140f-59ab-43e1-a397-56283f806b0b · outbound

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

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Biot: Biosignal transformer for cross-data learning in the wild

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:44.168581Z

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Observation 44341292-bb69-4a72-aef4-b0bc739d5bf8 · outbound

This paper cites DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:44.247675Z

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Observation 1bb94545-1be5-4583-9498-cbc8751e11d2 · outbound

This paper cites LUNA: Efficient and topology-agnostic foundation model for EEG signal analysis.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices LUNA: Efficient and topology-agnostic foundation model for EEG signal analysis

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:44.339613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0a89f7c-dbd7-4452-8244-f08ba10863d2 · outbound

This paper cites FEMBA: Efficient and Scalable EEGAnalysiswithaBidirectionalMambaFoundationModel.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices FEMBA: Efficient and Scalable EEGAnalysiswithaBidirectionalMambaFoundationModel

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:44.395294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c12d1e9b-c931-4551-8db6-b9400f38f0e8 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Distilling the Knowledge in a Neural Network

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:44.479074Z

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Unavailable: canonical work link unavailable.

Observation 83100f66-eb51-4cb0-bc9d-3dc7d7b64f0a · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:44.582407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cea5fbf7-5d02-4cc8-aa45-b2ac79ab7e4f · outbound

This paper cites Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:44.708622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26696884-2707-49e0-9f96-f33db54f64c6 · outbound

This paper cites MuBiNN: Multi-level bina- rized recurrent neural network for EEG signal classification.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices MuBiNN: Multi-level bina- rized recurrent neural network for EEG signal classification

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:44.786151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98f893be-03c0-42a8-ac31-1745b0205745 · outbound

This paper cites A dataset of scalp EEG recordings of Alzheimer’s disease, frontotemporal dementia and healthy subjects from routine EEG.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices A dataset of scalp EEG recordings of Alzheimer’s disease, frontotemporal dementia and healthy subjects from routine EEG

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:44.865736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19198945-d4d7-45c0-ba6b-b7ea1d233b96 · outbound

This paper cites Investigating Critical Frequency Bands and Channels for EEG- based Emotion Recognition with Deep Neural Networks.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Investigating Critical Frequency Bands and Channels for EEG- based Emotion Recognition with Deep Neural Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:44.961330Z

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Unavailable: canonical work link unavailable.

Observation 8be3091d-3a52-4cd7-85c4-335503ff629f · outbound

This paper cites Identifying similarities and differences in emotion recognition with EEG and eye movements among Chinese, German, and French People.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Identifying similarities and differences in emotion recognition with EEG and eye movements among Chinese, German, and French People

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:45.032879Z

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Unavailable: canonical work link unavailable.

Observation b2bec26e-141e-4136-96b7-172441d994e2 · outbound

This paper cites A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:45.134561Z

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Observation 9a2a0af6-045e-40ed-a17e-c6fbc3e965a0 · outbound

This paper cites Thermometer encoding: One hot way to resist adversarial examples.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Thermometer encoding: One hot way to resist adversarial examples

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:45.189890Z

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Observation 514de890-8625-48d4-9e90-92a240993664 · outbound

This paper cites Post training 4-bit quantization of convolutional networks for rapid-deployment.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Post training 4-bit quantization of convolutional networks for rapid-deployment

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:45.279189Z

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Observation dcc6b29b-d150-4e4f-beb7-042b7b6322ce · outbound

This paper cites 3575392- r2.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices 3575392- r2

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:45.350284Z

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Observation bfab8966-8964-42a3-b5d3-be49c15eacb3 · outbound

This paper cites Device JNEEG to convert Jetson Nano to brain-Computer interfaces. Short report.

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices Device JNEEG to convert Jetson Nano to brain-Computer interfaces. Short report

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T15:53:45.442161Z

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Pith citing papers

No inbound Pith citation observations are available.