A foundation model with multi-variate parallel attention to generate neuronal activity | Paper Citation Record

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This paper cites Time-LLM: Time series forecasting by reprogramming large language models,.

A foundation model with multi-variate parallel attention to generate neuronal activity Time-LLM: Time series forecasting by reprogramming large language models,

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This paper cites Timemixer: Decomposable multiscale mixing for time series forecasting,.

A foundation model with multi-variate parallel attention to generate neuronal activity Timemixer: Decomposable multiscale mixing for time series forecasting,

Reference 2

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This paper cites A time series is worth 64 words: Long- term forecasting with transformers,.

A foundation model with multi-variate parallel attention to generate neuronal activity A time series is worth 64 words: Long- term forecasting with transformers,

Reference 3

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A foundation model with multi-variate parallel attention to generate neuronal activity Unresolved cited work

Reference 4

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This paper cites Seizure prediction — ready for a new era,.

A foundation model with multi-variate parallel attention to generate neuronal activity Seizure prediction — ready for a new era,

Reference 5

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This paper cites Comparison of different input modalities and network structures for deep learning-based seizure detection,.

A foundation model with multi-variate parallel attention to generate neuronal activity Comparison of different input modalities and network structures for deep learning-based seizure detection,

Reference 6

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This paper cites EEGWaveNet: Multiscale CNN-based spatiotemporal feature extraction for EEG seizure detection,.

A foundation model with multi-variate parallel attention to generate neuronal activity EEGWaveNet: Multiscale CNN-based spatiotemporal feature extraction for EEG seizure detection,

Reference 7

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This paper cites Brain- BERT: Self-supervised representation learning for intracranial recordings,.

A foundation model with multi-variate parallel attention to generate neuronal activity Brain- BERT: Self-supervised representation learning for intracranial recordings,

Reference 8

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This paper cites Towards trustworthy seizure onset detection using workflow notes,.

A foundation model with multi-variate parallel attention to generate neuronal activity Towards trustworthy seizure onset detection using workflow notes,

Reference 9

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This paper cites Mul- ticenter intracranial EEG dataset for classification of graphoelements and artifactual signals,.

A foundation model with multi-variate parallel attention to generate neuronal activity Mul- ticenter intracranial EEG dataset for classification of graphoelements and artifactual signals,

Reference 10

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This paper cites Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli,.

A foundation model with multi-variate parallel attention to generate neuronal activity Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli,

Reference 11

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This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting,.

A foundation model with multi-variate parallel attention to generate neuronal activity Informer: Beyond efficient transformer for long sequence time-series forecasting,

Reference 12

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This paper cites Autoformer: Decomposition transformers with Auto- Correlation for long-term series forecasting,.

A foundation model with multi-variate parallel attention to generate neuronal activity Autoformer: Decomposition transformers with Auto- Correlation for long-term series forecasting,

Reference 13

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This paper cites Deep time series forecasting models: A comprehensive survey,.

A foundation model with multi-variate parallel attention to generate neuronal activity Deep time series forecasting models: A comprehensive survey,

Reference 14

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

A foundation model with multi-variate parallel attention to generate neuronal activity Brant-2: Foundation model for brain signals,

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This paper cites Attention is all you need,.

A foundation model with multi-variate parallel attention to generate neuronal activity Attention is all you need,

Reference 16

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

A foundation model with multi-variate parallel attention to generate neuronal activity An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 17

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This paper cites Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting,.

A foundation model with multi-variate parallel attention to generate neuronal activity Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting,

Reference 18

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This paper cites Transformers in Time Series: A Survey.

A foundation model with multi-variate parallel attention to generate neuronal activity Transformers in Time Series: A Survey

Reference 19

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This paper cites Transformer-XL: Attentive language models beyond a fixed-length context,.

A foundation model with multi-variate parallel attention to generate neuronal activity Transformer-XL: Attentive language models beyond a fixed-length context,

Reference 20

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This paper cites Generating Long Sequences with Sparse Transformers.

A foundation model with multi-variate parallel attention to generate neuronal activity Generating Long Sequences with Sparse Transformers

Reference 21

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This paper cites GQA: Training generalized multi-query transformer models from multi-head checkpoints,.

A foundation model with multi-variate parallel attention to generate neuronal activity GQA: Training generalized multi-query transformer models from multi-head checkpoints,

Reference 22

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This paper cites FlashAttention: Fast and memory-efficient exact attention with IO-awareness,.

A foundation model with multi-variate parallel attention to generate neuronal activity FlashAttention: Fast and memory-efficient exact attention with IO-awareness,

Reference 23

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A foundation model with multi-variate parallel attention to generate neuronal activity FlashAttention-2: Faster attention with better parallelism and work partitioning,

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A foundation model with multi-variate parallel attention to generate neuronal activity A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27,

Reference 25

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A foundation model with multi-variate parallel attention to generate neuronal activity LLM Pretraining with Continuous Concepts

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A foundation model with multi-variate parallel attention to generate neuronal activity GIVT: Generative infinite-vocabulary trans- formers,

Reference 27

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This paper cites Training Large Language Models to Reason in a Continuous Latent Space.

A foundation model with multi-variate parallel attention to generate neuronal activity Training Large Language Models to Reason in a Continuous Latent Space

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A foundation model with multi-variate parallel attention to generate neuronal activity Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

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A foundation model with multi-variate parallel attention to generate neuronal activity LoRA: Low-rank adaptation of large language models,

Reference 30

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A foundation model with multi-variate parallel attention to generate neuronal activity Analysis of EEG records in an epileptic patient using wavelet transform,

Reference 31

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A foundation model with multi-variate parallel attention to generate neuronal activity An EEG based real-time epilepsy seizure detection approach using discrete wavelet transform and machine learning methods,

Reference 32

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This paper cites wav2vec: Unsupervised pre-training for speech recognition,.

A foundation model with multi-variate parallel attention to generate neuronal activity wav2vec: Unsupervised pre-training for speech recognition,

Reference 33

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This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

A foundation model with multi-variate parallel attention to generate neuronal activity Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 34

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This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

A foundation model with multi-variate parallel attention to generate neuronal activity Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

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A foundation model with multi-variate parallel attention to generate neuronal activity Review of the BCI competition IV,

Reference 36

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This paper cites CHB-MIT scalp EEG database,.

A foundation model with multi-variate parallel attention to generate neuronal activity CHB-MIT scalp EEG database,

Reference 37

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This paper cites The temple university hospital eeg data corpus,.

A foundation model with multi-variate parallel attention to generate neuronal activity The temple university hospital eeg data corpus,

Reference 38

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This paper cites Population Transformer: Learning population-level representations of neural activity,.

A foundation model with multi-variate parallel attention to generate neuronal activity Population Transformer: Learning population-level representations of neural activity,

Reference 39

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This paper cites Interrater reliability between scorers from eight european sleep laboratories in subjects with different sleep disorders,.

A foundation model with multi-variate parallel attention to generate neuronal activity Interrater reliability between scorers from eight european sleep laboratories in subjects with different sleep disorders,

Reference 40

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This paper cites Characterization of four-class motor imagery EEG data for the BCI-competition 2005,.

A foundation model with multi-variate parallel attention to generate neuronal activity Characterization of four-class motor imagery EEG data for the BCI-competition 2005,

Reference 41

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A foundation model with multi-variate parallel attention to generate neuronal activity Interrater reliability: the kappa statistic,

Reference 42

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This paper cites The measurement of observer agreement for categorical data,.

A foundation model with multi-variate parallel attention to generate neuronal activity The measurement of observer agreement for categorical data,

Reference 43

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This paper cites Inter-rater agreement on identification of electrographic seizures and periodic discharges in icu eeg recordings,.

A foundation model with multi-variate parallel attention to generate neuronal activity Inter-rater agreement on identification of electrographic seizures and periodic discharges in icu eeg recordings,

Reference 44

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This paper cites EEG interpretation reliability and interpreter confidence: A large single-center study,.

A foundation model with multi-variate parallel attention to generate neuronal activity EEG interpretation reliability and interpreter confidence: A large single-center study,

Reference 45

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This paper cites Interrater reliability in interpretation of electrocorticographic seizure detections of the responsive neurostimulator,.

A foundation model with multi-variate parallel attention to generate neuronal activity Interrater reliability in interpretation of electrocorticographic seizure detections of the responsive neurostimulator,

Reference 46

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This paper cites Brant: Foundation model for intracranial neural signal,.

A foundation model with multi-variate parallel attention to generate neuronal activity Brant: Foundation model for intracranial neural signal,

Reference 47

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This paper cites A decoder-only foundation model for time-series forecasting,.

A foundation model with multi-variate parallel attention to generate neuronal activity A decoder-only foundation model for time-series forecasting,

Reference 48

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This paper cites Wpmixer: Efficient multi-resolution mixing for long-term time series forecasting,.

A foundation model with multi-variate parallel attention to generate neuronal activity Wpmixer: Efficient multi-resolution mixing for long-term time series forecasting,

Reference 49

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This paper cites Conformer: Convolution-augmented Transformer for Speech Recognition.

A foundation model with multi-variate parallel attention to generate neuronal activity Conformer: Convolution-augmented Transformer for Speech Recognition

Reference 50

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This paper cites FEDformer: Frequency enhanced de- composed transformer for long-term series forecasting,.

A foundation model with multi-variate parallel attention to generate neuronal activity FEDformer: Frequency enhanced de- composed transformer for long-term series forecasting,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.438599Z

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This paper cites Scaling data-constrained language models,.

A foundation model with multi-variate parallel attention to generate neuronal activity Scaling data-constrained language models,

Reference 52

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This paper cites Training compute-optimal large language models,.

A foundation model with multi-variate parallel attention to generate neuronal activity Training compute-optimal large language models,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.404426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a0e4c7c-d35f-4171-916c-157fffdb9a54 · outbound

This paper cites Scaling Laws for Neural Language Models.

A foundation model with multi-variate parallel attention to generate neuronal activity Scaling Laws for Neural Language Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T22:55:46.439690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40861744-3eba-4dab-bf48-9d620db55024 · outbound

This paper cites The fineweb datasets: Decanting the web for the finest text data at scale,.

A foundation model with multi-variate parallel attention to generate neuronal activity The fineweb datasets: Decanting the web for the finest text data at scale,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.386410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4846d29f-8c74-45f3-8373-a2025648a0b7 · outbound

This paper cites The case for cleaner biosignals: High-fidelity neural compressor enables transfer from cleaner iEEG to noisier EEG,.

A foundation model with multi-variate parallel attention to generate neuronal activity The case for cleaner biosignals: High-fidelity neural compressor enables transfer from cleaner iEEG to noisier EEG,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.370650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d2a11861-8dcc-4dad-8cf3-1ed115d94f93 · outbound

This paper cites Artificial intelligence as an emerging technology in the current care of neurological disorders,.

A foundation model with multi-variate parallel attention to generate neuronal activity Artificial intelligence as an emerging technology in the current care of neurological disorders,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.350499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b15427a1-1c50-4929-a03f-20272467ef83 · outbound

This paper cites Long-term treatment with responsive brain stimulation in adults with refractory partial seizures,.

A foundation model with multi-variate parallel attention to generate neuronal activity Long-term treatment with responsive brain stimulation in adults with refractory partial seizures,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.334095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0b80185f-97df-4292-9158-d9cffaf0d061 · outbound

This paper cites Wearable digital health technology for epilepsy,.

A foundation model with multi-variate parallel attention to generate neuronal activity Wearable digital health technology for epilepsy,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.317319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 02666e0f-5cf9-4bd8-81ff-a67913af52ca · outbound

This paper cites Emerging insights into the genesis of epilepsy,.

A foundation model with multi-variate parallel attention to generate neuronal activity Emerging insights into the genesis of epilepsy,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.297717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2198d99c-a2ea-4260-92f6-50a9bbe2106e · outbound

This paper cites Patient-independent seizure detection based on long-term iEEG and a novel lightweight CNN,.

A foundation model with multi-variate parallel attention to generate neuronal activity Patient-independent seizure detection based on long-term iEEG and a novel lightweight CNN,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.283289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ca67d299-c94c-4dc6-8161-d99a98424533 · outbound

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

A foundation model with multi-variate parallel attention to generate neuronal activity Neuro-GPT: Towards A Foundation Model for EEG

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T22:55:46.485731Z

Source-reported events for the cited work

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Observation 33b423c7-f6f6-4ad8-a41c-ccee641c9dca · outbound

This paper cites DeBERTa: Decoding-enhanced bert with disentangled attention,.

A foundation model with multi-variate parallel attention to generate neuronal activity DeBERTa: Decoding-enhanced bert with disentangled attention,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.266879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8c02be65-c372-4251-8bf5-4b8391e30858 · outbound

This paper cites Axial Attention in Multidimensional Transformers.

A foundation model with multi-variate parallel attention to generate neuronal activity Axial Attention in Multidimensional Transformers

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T22:55:46.497548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 551c1254-320a-455e-8c93-a71bf964ddb6 · outbound

This paper cites CCNet: Criss-cross attention for semantic segmentation,.

A foundation model with multi-variate parallel attention to generate neuronal activity CCNet: Criss-cross attention for semantic segmentation,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.251551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e16f954c-04ec-462b-a748-3a46ae5effc9 · outbound

This paper cites Reformer: The efficient transformer,.

A foundation model with multi-variate parallel attention to generate neuronal activity Reformer: The efficient transformer,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.236825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6c95e653-a586-4060-8c81-bf903d9d86ce · outbound

This paper cites Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX,.

A foundation model with multi-variate parallel attention to generate neuronal activity Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.221360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5bae6d6d-d683-47ca-b44e-2786313eb6e0 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

A foundation model with multi-variate parallel attention to generate neuronal activity Representation Learning with Contrastive Predictive Coding

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T22:55:46.524673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94f7c990-7007-4d26-a920-c41a3fa99d75 · outbound

This paper cites Neurons that fire together also conspire together: Is normal sleep circuitry hijacked to generate epilepsy?.

A foundation model with multi-variate parallel attention to generate neuronal activity Neurons that fire together also conspire together: Is normal sleep circuitry hijacked to generate epilepsy?

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.205958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3d3c0469-99cb-42fb-b78e-ef7f94e980a2 · outbound

This paper cites Controversies on the network theory of epilepsy: Debates held during the ictals 2019 conference,.

A foundation model with multi-variate parallel attention to generate neuronal activity Controversies on the network theory of epilepsy: Debates held during the ictals 2019 conference,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.190284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2a5f9f9f-9430-4a37-b039-85b3f55beb25 · outbound

This paper cites Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings,.

A foundation model with multi-variate parallel attention to generate neuronal activity Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.175499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e592a7f1-ed27-4c8f-bb91-767347cb1587 · outbound

This paper cites Objective evaluation metrics for automatic classification of EEG events.

A foundation model with multi-variate parallel attention to generate neuronal activity Objective evaluation metrics for automatic classification of EEG events

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T22:55:46.555829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7b13e9b-e7ac-4936-b1ef-f5342519ab2c · outbound

This paper cites Validation of temporal scoring metrics for automatic seizure detection,.

A foundation model with multi-variate parallel attention to generate neuronal activity Validation of temporal scoring metrics for automatic seizure detection,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.159911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8be99804-9848-4cdb-ad8d-86e220afd965 · outbound

This paper cites Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures,.

A foundation model with multi-variate parallel attention to generate neuronal activity Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.142891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4dbb741b-92a4-4820-b9e9-0c8698637e44 · outbound

This paper cites Seizure detection at home: Do devices on the market match the needs of people living with epilepsy and their caregivers?.

A foundation model with multi-variate parallel attention to generate neuronal activity Seizure detection at home: Do devices on the market match the needs of people living with epilepsy and their caregivers?

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.127868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f6fc7ca9-0358-4ff1-b30f-a3477a31ba19 · outbound

This paper cites Intracranial eeg seizure onset and termination patterns and their association,.

A foundation model with multi-variate parallel attention to generate neuronal activity Intracranial eeg seizure onset and termination patterns and their association,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.113205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9e659925-16e4-4860-a0c3-f84afacf6b29 · outbound

This paper cites A few thoughts on “what is a seizure?.

A foundation model with multi-variate parallel attention to generate neuronal activity A few thoughts on “what is a seizure?

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.096414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dd3c1aab-2047-4174-88a5-2816ef0da930 · outbound

This paper cites Structural, geometric and genetic factors predict interregional brain connectivity patterns probed by electrocorticography,.

A foundation model with multi-variate parallel attention to generate neuronal activity Structural, geometric and genetic factors predict interregional brain connectivity patterns probed by electrocorticography,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.081577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2158c174-71dc-41bd-a3bb-43c431a83c33 · outbound

This paper cites Geometric constraints on human brain function,.

A foundation model with multi-variate parallel attention to generate neuronal activity Geometric constraints on human brain function,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.062957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f2d58510-ecfe-4bfc-9303-0600cbbe1277 · outbound

This paper cites Report of the committee on methods of clinical examination in electroen- cephalography: 1957,.

A foundation model with multi-variate parallel attention to generate neuronal activity Report of the committee on methods of clinical examination in electroen- cephalography: 1957,

Reference 80

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T22:55:47.046751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fabbf2e1-86fb-462a-ac71-4aa0d12e4cc2 · outbound

This paper cites In bold are the best MSE results, in italics are the second best.

A foundation model with multi-variate parallel attention to generate neuronal activity In bold are the best MSE results, in italics are the second best

Reference 720

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:55:47.030964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers