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Paper Citation Record · LEDGER

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data

As of 13 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2412.17833.

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

pith.paper-citation-record.v1
2412.17833 v1

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measured 31 of 31 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation 24a21682-ae53-45c5-ac7b-5c4ce77e8eac · outbound

This paper cites Progress in brain computer in- terface: Challenges and opportunities,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Progress in brain computer in- terface: Challenges and opportunities,

Reference 1

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This paper cites A novel deep learning scheme for motor imagery eeg decoding based on spatial representation fusion,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data A novel deep learning scheme for motor imagery eeg decoding based on spatial representation fusion,

Reference 2

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This paper cites Fuzzy temporal convolutional neural networks in p300-based brain–computer interface for smart home interaction,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Fuzzy temporal convolutional neural networks in p300-based brain–computer interface for smart home interaction,

Reference 3

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This paper cites Improving speller bci performance using a cluster-based under-sampling method,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Improving speller bci performance using a cluster-based under-sampling method,

Reference 4

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Observation 7f0712be-e581-419a-a9d5-08bd8cd98b80 · outbound

This paper cites Under-sampling and classification of p300 single-trials using self-organized maps and deep neural networks for a speller bci,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Under-sampling and classification of p300 single-trials using self-organized maps and deep neural networks for a speller bci,

Reference 5

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This paper cites The application of transfer learning in p300 detection,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data The application of transfer learning in p300 detection,

Reference 6

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Observation 99dfae93-2e36-462e-8cb6-7ccb1d4e9189 · outbound

This paper cites Cross- domain mlp and cnn transfer learning for biological signal processing: Eeg and emg,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Cross- domain mlp and cnn transfer learning for biological signal processing: Eeg and emg,

Reference 7

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This paper cites A deep transfer convolutional neural network framework for eeg signal classification,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data A deep transfer convolutional neural network framework for eeg signal classification,

Reference 8

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This paper cites Classification and transfer learning of eeg during a kinesthetic motor imagery task using deep convolutional neural networks,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Classification and transfer learning of eeg during a kinesthetic motor imagery task using deep convolutional neural networks,

Reference 9

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Observation d0e7b36f-bf47-4ae7-8b5f-69d02862ea7f · outbound

This paper cites Adaptive transfer learning for eeg motor imagery classification with deep convolutional neural network,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Adaptive transfer learning for eeg motor imagery classification with deep convolutional neural network,

Reference 10

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This paper cites Accelerating minibatch stochastic gradient descent using stratified sampling,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Accelerating minibatch stochastic gradient descent using stratified sampling,

Reference 11

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This paper cites Posterior transfer learning with active sampling,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Posterior transfer learning with active sampling,

Reference 12

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This paper cites Imbalanced data classification: Using transfer learning and active sampling,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Imbalanced data classification: Using transfer learning and active sampling,

Reference 13

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Observation af7d61a2-4362-4470-80b7-7179f64ea6d5 · outbound

This paper cites Positive-negative momen- tum: Manipulating stochastic gradient noise to improve generalization,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Positive-negative momen- tum: Manipulating stochastic gradient noise to improve generalization,

Reference 14

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This paper cites Kulesza and B.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Kulesza and B

Reference 15

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This paper cites k-dpps: Fixed-size determinantal point processes,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data k-dpps: Fixed-size determinantal point processes,

Reference 16

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Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Determinantal point processes for machine learning,

Reference 17

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This paper cites Diverse mini-batch Active Learning.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Diverse mini-batch Active Learning

Reference 18

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This paper cites Determinantal Point Processes for Mini-Batch Diversification.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Determinantal Point Processes for Mini-Batch Diversification

Reference 19

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Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Active mini-batch sampling using repulsive point processes,

Reference 20

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This paper cites A comparison of methods for generating poisson disk distributions,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data A comparison of methods for generating poisson disk distributions,

Reference 21

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This paper cites An efficient p300-based brain–computer interface for disabled subjects,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data An efficient p300-based brain–computer interface for disabled subjects,

Reference 22

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This paper cites Perfor- mance evaluation of a p300 brain-computer interface using a kernel extreme learning machine classifier,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Perfor- mance evaluation of a p300 brain-computer interface using a kernel extreme learning machine classifier,

Reference 23

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This paper cites Deep learning techniques for eeg signal applications–a review,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Deep learning techniques for eeg signal applications–a review,

Reference 24

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Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Brain–computer interfaces for communication and control,

Reference 25

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This paper cites A gentle introduction and survey on computing with words (cww) methodologies,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data A gentle introduction and survey on computing with words (cww) methodologies,

Reference 26

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This paper cites Towards understanding human functional brain development with explainable artificial intelligence: Challenges and perspectives,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Towards understanding human functional brain development with explainable artificial intelligence: Challenges and perspectives,

Reference 27

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This paper cites Real time recognition of human activities from wearable sensors by evolving classifiers,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Real time recognition of human activities from wearable sensors by evolving classifiers,

Reference 28

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Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Effective brain connectivity for fnirs with fuzzy cog- nitive maps in neuroergonomics,

Reference 29

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This paper cites Deep learning towards intelligent vehicle fault diagnosis,.

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Deep learning towards intelligent vehicle fault diagnosis,

Reference 30

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Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data Single-trial recognition of video gamer’s expertise from brain haemodynamic and facial emotion re- sponses,

Reference 31

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Pith citing papers

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