The more, the better? Evaluating the role of EEG preprocessing for deep learning applications | Paper Citation Record

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This paper cites A com- prehensive review of EEG-based brain-computer interface paradigms,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications A com- prehensive review of EEG-based brain-computer interface paradigms,

Reference 1

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Observation ad80c38b-e0d3-4591-a74f-4c6cd821c6a7 · outbound

This paper cites EEG based emotion recognition: A tutorial and review,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications EEG based emotion recognition: A tutorial and review,

Reference 2

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Observation d93892f4-3593-413d-a3b0-d62f75d4b135 · outbound

This paper cites Sleep stage classification using EEG signal analysis: a compre- hensive survey and new investigation,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Sleep stage classification using EEG signal analysis: a compre- hensive survey and new investigation,

Reference 3

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This paper cites Machine learning for predicting epileptic seizures using EEG signals: A review,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Machine learning for predicting epileptic seizures using EEG signals: A review,

Reference 4

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This paper cites Survey of machine learning techniques in the analysis of EEG signals for Parkinson’s disease: A systematic review,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Survey of machine learning techniques in the analysis of EEG signals for Parkinson’s disease: A systematic review,

Reference 5

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This paper cites Deep learning in the EEG diagnosis of Alzheimer’s disease,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Deep learning in the EEG diagnosis of Alzheimer’s disease,

Reference 6

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This paper cites Deep learning for electroen- cephalogram (EEG) classification tasks: a review,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Deep learning for electroen- cephalogram (EEG) classification tasks: a review,

Reference 7

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This paper cites Deep learning,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Deep learning,

Reference 8

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Observation e270e2b9-c2f1-4885-8681-904a7042db7d · outbound

This paper cites EEG datasets for health- care: a scoping review,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications EEG datasets for health- care: a scoping review,

Reference 9

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This paper cites The harvard automated processing pipeline for electroen- cephalography (HAPPE): standardized processing software for devel- opmental and high-artifact data,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications The harvard automated processing pipeline for electroen- cephalography (HAPPE): standardized processing software for devel- opmental and high-artifact data,

Reference 10

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Observation ec259ad9-6efe-42b8-a796-f82cf077525e · outbound

This paper cites Automagic: Standardized preprocessing of big EEG data,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Automagic: Standardized preprocessing of big EEG data,

Reference 11

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Observation 9b9108df-cd98-421e-8fb7-2d17c7b9e430 · outbound

This paper cites BID- SAlign: a library for automatic merging and preprocessing of multiple EEG repositories,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications BID- SAlign: a library for automatic merging and preprocessing of multiple EEG repositories,

Reference 12

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This paper cites Self-supervised learning for electroencephalography,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Self-supervised learning for electroencephalography,

Reference 13

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This paper cites Deep learning-based electroencephalography analysis: a systematic review,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Deep learning-based electroencephalography analysis: a systematic review,

Reference 14

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This paper cites On the classification of SSVEP-based dry-EEG signals via convolutional neural networks,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications On the classification of SSVEP-based dry-EEG signals via convolutional neural networks,

Reference 15

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This paper cites Eeg-signals based cognitive workload detection of vehicle driver using deep learning,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Eeg-signals based cognitive workload detection of vehicle driver using deep learning,

Reference 16

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Observation 2bbbc1cb-3c64-4359-b72a-db0b2fbc9b22 · outbound

This paper cites Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces,

Reference 17

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Observation d3f43724-738a-4d6d-8c20-8656d1b12cbe · outbound

This paper cites Cross-participant EEG-based assessment of cognitive work- load using multi-path convolutional recurrent neural networks,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Cross-participant EEG-based assessment of cognitive work- load using multi-path convolutional recurrent neural networks,

Reference 18

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This paper cites On EEG preprocessing role in deep learning effectiveness for mental workload classification,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications On EEG preprocessing role in deep learning effectiveness for mental workload classification,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:18:00.831603Z

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This paper cites The OpenNeuro resource for sharing of neuroscience data,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications The OpenNeuro resource for sharing of neuroscience data,

Reference 20

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This paper cites American clinical neurophysiology society guideline 1: minimum technical requirements for performing clinical electroencephalography,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications American clinical neurophysiology society guideline 1: minimum technical requirements for performing clinical electroencephalography,

Reference 21

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This paper cites Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review,

Reference 22

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Observation 52553a63-8b1a-4a41-955a-179b6f0ef8d7 · outbound

This paper cites A test-retest resting and cognitive state EEG dataset,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications A test-retest resting and cognitive state EEG dataset,

Reference 23

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

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications A dataset of EEG recordings from: Alzheimer’s disease, frontotemporal dementia and healthy subjects,

Reference 24

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This paper cites Deep comparisons of neural networks from the EEGNet family,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Deep comparisons of neural networks from the EEGNet family,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:18:00.770019Z

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Observation c9d04f77-1d62-4bc1-a7af-4b9c981002d8 · outbound

This paper cites Motor imagery decoding using ensemble curriculum learning and collaborative training,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Motor imagery decoding using ensemble curriculum learning and collaborative training,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:18:00.754871Z

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Observation 97b511f0-2d2c-4e69-b030-1b39ff8aaf67 · outbound

This paper cites UC San Diego resting state EEG data from patients with Parkinson’s disease,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications UC San Diego resting state EEG data from patients with Parkinson’s disease,

Reference 27

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This paper cites EEG: 3-stim auditory oddball and rest in Parkinson’s,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications EEG: 3-stim auditory oddball and rest in Parkinson’s,

Reference 28

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This paper cites A resting- state EEG dataset for sleep deprivation,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications A resting- state EEG dataset for sleep deprivation,

Reference 29

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This paper cites EEG: First episode psychosis vs. control resting task 2,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications EEG: First episode psychosis vs. control resting task 2,

Reference 30

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This paper cites EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,

Reference 31

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This paper cites An information-maximization approach to blind separation and blind deconvolution,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications An information-maximization approach to blind separation and blind deconvolution,

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This paper cites ICLabel: An automated electroencephalographic independent component classifier, dataset, and website,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications ICLabel: An automated electroencephalographic independent component classifier, dataset, and website,

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This paper cites Real-time neuroimaging and cognitive monitoring using wearable dry EEG,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Real-time neuroimaging and cognitive monitoring using wearable dry EEG,

Reference 34

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This paper cites Distortions in EEG interregional phase synchrony by spherical spline interpolation: causes and remedies,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Distortions in EEG interregional phase synchrony by spherical spline interpolation: causes and remedies,

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This paper cites On the effects of data normalization for domain adaptation on EEG data,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications On the effects of data normalization for domain adaptation on EEG data,

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This paper cites SelfEEG: A Python library for self-supervised learning in electroen- cephalography,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications SelfEEG: A Python library for self-supervised learning in electroen- cephalography,

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This paper cites Pytorch: An imperative style, high-performance deep learning library,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Pytorch: An imperative style, high-performance deep learning library,

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This paper cites SciPy 1.0: fundamental algorithms for scientific computing in python,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications SciPy 1.0: fundamental algorithms for scientific computing in python,

Reference 39

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This paper cites Seaborn: statistical data visualization,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Seaborn: statistical data visualization,

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This paper cites Statannotations,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Statannotations,

Reference 41

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

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces,

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

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Deep learning with convolutional neural networks for EEG decoding and visualization,

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This paper cites A multi- view CNN with novel variance layer for motor imagery brain computer interface,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications A multi- view CNN with novel variance layer for motor imagery brain computer interface,

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This paper cites Subject-Aware Contrastive Learning for Biosignals.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Subject-Aware Contrastive Learning for Biosignals

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This paper cites The necessity of leave one subject out (LOSO) cross validation for EEG disease diagnosis,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications The necessity of leave one subject out (LOSO) cross validation for EEG disease diagnosis,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:18:00.541232Z

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This paper cites On over-fitting in model selection and subsequent selection bias in performance evaluation,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications On over-fitting in model selection and subsequent selection bias in performance evaluation,

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This paper cites Adam: A method for stochastic optimization,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Adam: A method for stochastic optimization,

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This paper cites Individual comparisons by ranking methods,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Individual comparisons by ranking methods,

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This paper cites On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other,

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This paper cites A simple sequentially rejective multiple test procedure,.

The more, the better? Evaluating the role of EEG preprocessing for deep learning applications A simple sequentially rejective multiple test procedure,

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The more, the better? Evaluating the role of EEG preprocessing for deep learning applications The use of ranks to avoid the assumption of normality implicit in the analysis of variance,

Reference 52

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The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Statistical comparisons of classifiers over multiple data sets,

Reference 53

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The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Model evaluation, model selection, and algorithm selection in machine learning,

Reference 54

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The more, the better? Evaluating the role of EEG preprocessing for deep learning applications The effect of preprocessing techniques, applied to numeric features, on classification algorithms’ performance,

Reference 55

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The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Unresolved cited work

Reference 56

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The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Approximate statistical tests for comparing supervised classification learning algorithms,

Reference 57

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The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Available: https://doi.org/10.3390/data6020011

Reference 58

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The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Inference for the generalization error,

Reference 61

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The more, the better? Evaluating the role of EEG preprocessing for deep learning applications to(device=‘cuda’)

Reference 62

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The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Unresolved cited work

Reference 63

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The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Data Partition

Reference 64

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The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Available: http://jmlr.org/papers/v7/demsar06a.html

Reference 2006

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The more, the better? Evaluating the role of EEG preprocessing for deep learning applications Available: https://doi.org/10.18112/openneuro.ds003490

Reference 2021

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