Analysis of cognitive mechanisms in phoneme perception and pronunciation errors among Korean language learners.
The 9 matches
- [1] § Experimental setup › Training dynamics and convergence analysis ↔ src/EEGNet_Hybrid.ipynb, lines 200–306 · score 0.82 · optimization step, training accuracy, EEGNet, validation loss, model training, gradient
- [2] § Experimental setup › Experimental details ↔ src/ShallowDeep.ipynb, lines 39–111 · score 0.78 · cosine annealing, squared error, MSE, schedule, regression, classification
- [3] § Experimental setup › Comparison with SOTA methods ↔ src/examples/EEGModels.py, lines 362–403 · score 0.70 · ShallowConvNet, convolutional networks, EEG models, deviation, EEG signal, filtering
- [4] § Experimental setup › Training dynamics and convergence analysis ↔ src/examples/MNIST_Early_Stopping_example.ipynb, lines 117–197 · score 0.68 · optimization step, validation loss, model training, gradient, epochs, prediction
- [5] § Experimental setup › Experimental details ↔ src/ShallowDeep.ipynb, lines 39–111 · score 0.63 · Weight decay, cropping, PyTorch, Adam, deep, batch
- [6] § Experimental setup › Dataset ↔ src/examples/EEGNet TF.ipynb, lines 1–88 · score 0.63 · brain computer interfaces, neuroscience, auditory, neural, filtering, epoch
- [7] § Experimental setup › Dataset ↔ src/examples/ERP.py, lines 1–67 · score 0.63 · brain computer interfaces, neuroscience, auditory, neural, filtering, epoch
- [8] § Experimental setup › Experimental details ↔ src/examples/plot_bcic_iv_2a_moabb_trial.ipynb, lines 192–245 · score 0.57 · cosine annealing, schedule, classification, optimized, loss, validation
- [9] § Experimental setup › Experimental details ↔ src/examples/plot_bcic_iv_2a_moabb_trial.ipynb, lines 192–245 · score 0.56 · Weight decay, PyTorch, Adam, batch, optimizer, epochs
Paper
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The authors' code
Jupyter notebook · 347 lines · 36 KB · no license · 2 matches
ShallowDeep.ipynb at commit 2309398, no license · at the source
Overview
Abstract
Introduction: Cognitive load tracking in Korean phoneme recognition presents significant changes due to the intricate spatiotemporal dynamics of EEG signals and the inherent variability in cognitive states. Traditional methods often struggle with these complexities, leading to suboptimal performance in accurately modeling cognitive load. This paper introduces an innovative framework, the Adaptive EEG Attention Trac, designed to overcome these limitations by leveraging attention-augmented EEG signals.
Methods: The proposed methodology comprises three integral components: the Manifold Constrained Signal Encoding, the Agent-driven Temporal Attention Routing, and the Uncertainty-aware Cognitive Load Prediction. The encoder is responsible for transforming raw EEG signals into a compact latent representation while adhering to manifold constraints, thereby ensuring structural fidelity. The attention router dynamically allocates focus across temporal segments, enhancing both interpretability and relevance of the signals. The predictor incorporates uncertainty quantification, which is crucial for providing robust estimations of cognitive load. Furthermore, the Uncertainty Propagation Adjustment strategy is introduced to explicitly model and propagate uncertainty throughout the computational pipeline, thereby refining predictions and enhancing reliability.
Results and discussion: Experimental results substantiate the efficacy of the proposed framework, demonstrating its capability to accurately track cognitive load during Korean phoneme recognition tasks. This advancement significantly contributes to the field of EEG-based cognitive modeling, offering a more reliable and interpretable approach to understanding cognitive processes.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.
Khalizo/Deep-Learning-Detection-Of-EEG-Based-Attention
230939815ae58c75fdf5baf27a5954d559fef5b5, 7 February 2023Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
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- src/
.ipynb_checkpoints/ — Jupyter, 554 lines, shown from its sourceData_Processing-checkpoi nt.ipynb - src/
.ipynb_checkpoints/ — Jupyter, 202 lines, shown from its sourceData_Visualisation-check point.ipynb - src/
.ipynb_checkpoints/ — Jupyter, 625 lines, shown from its sourceEEGNet_Hybrid-checkpoint .ipynb - src/
.ipynb_checkpoints/ — Python, 1,420 lines, shown from its sourceEEG_Toolbox-checkpoint.p y - src/
.ipynb_checkpoints/ — Jupyter, 352 lines, shown from its sourceShallowDeep-checkpoint.i pynb - src/
.ipynb_checkpoints/ — Jupyter, 217 lines, shown from its sourcebaseline-checkpoint.ipyn b - src/
.ipynb_checkpoints/ — Jupyter, 221 lines, shown from its sourceevaluation-checkpoint.ip ynb - src/
.ipynb_checkpoints/ — Python, 50 lines, shown from its sourcepytorchtools-checkpoint. py - src/
Data_Processing.ipynb — Jupyter, 573 lines, shown from its source - src/
Data_Visualisation.ipynb — Jupyter, 199 lines, shown from its source - src/
EEGNet_Hybrid.ipynb — Jupyter, 605 lines, 1 match, shown from its source - src/
EEG_Toolbox.py — Python, 1,420 lines, shown from its source - src/
ShallowDeep.ipynb — Jupyter, 347 lines, 2 matches, shown from its source - src/
baseline.ipynb — Jupyter, 281 lines, shown from its source - src/
evaluation.ipynb — Jupyter, 221 lines, shown from its source - src/
examples/ — Jupyter, 171 lines, shown from its source.ipynb_checkpoints/ BrainNet-MyData-checkpoi nt.ipynb - src/
examples/ — Jupyter, 1 line, shown from its source.ipynb_checkpoints/ BrainNet-checkpoint.ipyn b - src/
examples/ — Python, 405 lines, shown from its source.ipynb_checkpoints/ EEGModels-checkpoint.py - src/
examples/ — Jupyter, 1 line, shown from its source.ipynb_checkpoints/ EEGNet TF-checkpoint.ipynb - src/
examples/ — Jupyter, 186 lines, shown from its source.ipynb_checkpoints/ EEGNet-PyTorchExample-ch eckpoint.ipynb - src/
examples/ — Jupyter, 1 line, shown from its source.ipynb_checkpoints/ EEG_Toolbox-checkpoint.i pynb - src/
examples/ — Python, 239 lines, shown from its source.ipynb_checkpoints/ ERP-checkpoint.py - src/
examples/ — Jupyter, 307 lines, shown from its source.ipynb_checkpoints/ MNIST_Early_Stopping_exa mple-checkpoint.ipynb - src/
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examples/ — Python, 511 lines, shown from its source.ipynb_checkpoints/ brain_typing-checkpoint. py - src/
examples/ — Jupyter, 293 lines, shown from its source.ipynb_checkpoints/ plot_bcic_iv_2a_moabb_tr ial-checkpoint.ipynb - src/
examples/ — Jupyter, 62 lines, shown from its source.ipynb_checkpoints/ plot_custom_dataset_exam ple-checkpoint.ipynb - src/
examples/ — Jupyter, 177 lines, shown from its sourceBrainNet-MyData.ipynb - src/
examples/ — Jupyter, 186 lines, shown from its sourceBrainNet.ipynb - src/
examples/ — Python, 405 lines, 1 match, shown from its sourceEEGModels.py - src/
examples/ — Jupyter, 164 lines, 1 match, shown from its sourceEEGNet TF.ipynb - src/
examples/ — Jupyter, 186 lines, shown from its sourceEEGNet-PyTorchExample.ip ynb - src/
examples/ — Jupyter, 1,422 lines, shown from its sourceEEG_Toolbox.ipynb - src/
examples/ — Python, 239 lines, 1 match, shown from its sourceERP.py - src/
examples/ — Jupyter, 307 lines, 1 match, shown from its sourceMNIST_Early_Stopping_exa mple.ipynb - src/
examples/ — Jupyter, 191 lines, shown from its sourcePytorch_Tutorial_1st_CNN .ipynb - src/
examples/ — Python, 511 lines, shown from its sourcebrain_typing.py - src/
examples/ — Jupyter, 294 lines, 2 matches, shown from its sourceplot_bcic_iv_2a_moabb_tr ial.ipynb - src/
examples/ — Jupyter, 66 lines, shown from its sourceplot_custom_dataset_exam ple.ipynb - src/
pytorchtools.py — Python, 50 lines, shown from its source - README.md — Text, 114 lines, shown from its source
Tracing map
Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.
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Data
Datasets cited
- data.mendeley.com/
datasets/ — at Mendeley Data; found in the resources tablekt38js3jv7 - github.com/
mcjpedro/ — at github.com; found in the resources tablespeech_decoding - openneuro:ds006104 — at OpenNeuro; found in the resources table
Data availability statement
The original contributions presented in the study are included in the article/
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 1 author, 5 keywords, 25 references.
Cite
This paper
Zhang, Y. (2026). Analysis of cognitive mechanisms in phoneme perception and pronunciation errors among Korean language learners. Frontiers in psychology, 17, 1774068. https://
BibTeX
@article{zhang2026analys
author = {Zhang, Yuwen},
title = {{Analysis of cognitive mechanisms in phoneme perception and pronunciation errors among Korean language learners}},
journal = {Frontiers in psychology},
year = {2026},
month = jun,
volume = {17},
pages = {1774068},
publisher = {Frontiers Media SA},
issn = {1664-1078},
doi = {10.3389/
url = {https://
pmid = {42376149},
pmcid = {PMC13311114}
}
RIS
TY - JOUR
AU - Zhang, Yuwen
TI - Analysis of cognitive mechanisms in phoneme perception and pronunciation errors among Korean language learners
T2 - Frontiers in psychology
J2 - Front Psychol
PY - 2026
DA - 2026/
VL - 17
SP - 1774068
SN - 1664-1078
PB - Frontiers Media SA
DO - 10.3389/
UR - https://
LA - en
ER -
CSL-JSON
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