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Bayesian Uncertainty-aware Deep Learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection.

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5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › Implementation and evaluation ↔ python/train/train_nal.py, lines 17–153 · score 0.63 · validation loss, PyTorch, finetuning, Adam, NAL, optimizer
  2. [2] § Materials and methods › Implementation and evaluation ↔ python/train/train_hvit.py, lines 19–134 · score 0.62 · validation loss, PyTorch, HViT, Adam, DUL, optimizer
  3. [3] § Materials and methods › EEG datasets › Simulated data. ↔ matlab/simulate/SEREEGA-master/pop/pop_sereega_plot_source_location.m, lines 40–131 · score 0.53 · source locations, lead field, SEREEGA, components, simulate
  4. [4] § Materials and methods › BUNDL: Bayesian Uncertainty-aware Deep Learning › Approximating label mismatch with . ↔ python/train/BUNDL.py, lines 73–156 · score 0.52 · cross entropy loss, ensemble, TTA, MCD, uncertainty, predictions
  5. [5] § Materials and methods › EEG datasets › Simulated data. ↔ matlab/simulate/get_signal.m, the whole file · a weak match · score 0.51 · sharp waves, polyspike, spikes, Seizure

Paper

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The authors' code

Python · 153 lines · 6.2 KB · no license · 1 match

  1. from models_nal import *
  2. from utils import *
  3. #from dataloader import *
  4. import numpy as np
  5. import pandas as pd
  6. import json
  7. import os
  8. import scipy
  9. import scipy.special
  10. import torch
  11. import torch.nn as nn
  12. import sklearn.metrics as metrics
  13. #from pyt_enc import *
  14. import torch.nn.functional as F
  15. detloss = nn.CrossEntropyLoss(weight = torch.Tensor([0.2, 0.8]).double().to('cuda:0'))
  17. def nal_finetune(data_root, modelname, mn_fn, cvfold,
  18. hparams = {'lr':1e-04, 'maxiter':100, 'prior':0.8, 'beta':1.0},
  19. traintype='s_model',
  20. use_cuda=False):
  21. lr = hparams['lr']
  22. maxiter = hparams['maxiter']
  23. beta = hparams['beta']
  24. prior = hparams['prior']
  25. device = 'cuda:0' if use_cuda and torch.cuda.is_available() else 'cpu'
  26. if use_cuda:
  27. torch.cuda.empty_cache()
  28. print(traintype, modelname)
  29. #detloss = nn.CrossEntropyLoss(weight = torch.Tensor([0.2, 0.8]).double().to(device))
  30. manifest = read_manifest(data_root+mn_fn, ',')
  31. save_loc = data_root+'split'+str(cvfold)+'/models/'
  32. if not os.path.exists(save_loc):
  33. os.mkdir(save_loc)
  35. pt_list = np.load(data_root+'all_pts.npy')
  37. train_set = myDataSet(data_root,cvfold, manifest,
  38. normalize=True,train=True,
  39. maxSeiz = 10)
  40. val_set = myDataSet(data_root,cvfold, manifest,
  41. normalize=True,train=False,
  42. maxSeiz = 10)
  43. train_loader = DataLoader(train_set, batch_size=1, shuffle=True)
  44. val_loader = DataLoader(val_set, batch_size=1, shuffle=True)
  46. s_model = True if traintype=='s_model' else False
  47. cm_theta = torch.tensor(np.load(modelname.split('_')[0]+str(cvfold)+'_cmtheta_pretrain_unc.npy')).double()
  48. if modelname == 'txlstm_nal':
  49. model = transformer_lstm_nal(transformer_dropout=0.2, device=device, s_model=s_model, cm_theta = cm_theta)
  50. elif modelname == 'sztrack_nal':
  51. model = sztrack_nal( s_model=s_model, cm_theta = cm_theta)
  52. elif modelname == 'tgcn_nal':
  53. model = TGCN_nal(s_model=s_model, cm_theta = cm_theta, device=device)
  54. elif modelname == 'tx3lstm_nal':
  55. model = transformer_3lstm(transformer_dropout=0.1, device=device)
  56. else:
  57. model = CNN_BLSTM_nal(s_model=s_model, cm_theta = cm_theta, device=device)
  60. savename = modelname+'_'+traintype+'_cv'+str(cvfold)+'_'+str(lr)
  61. oldlr = {'txlstm':1e-05, 'tgcn': 1e-06, 'cnnlstm':0.01, 'cnnblstm':0.01}
  63. ########## initialize with pretrained model, confusion matrix of pretrained or s_model
  64. if s_model:
  65. oldsavename = modelname.split('_')[0]+'_pretrain_unc_cv'+str(cvfold)+'_'+str(oldlr[modelname.split('_')[0]])
  66. state_dict = torch.load(save_loc + oldsavename+'.pth.tar')
  67. with torch.no_grad():
  69. for n, p in model.named_parameters():
  70. try:
  71. p.data = state_dict[n].double()
  72. except:
  73. continue
  75. else: #c_model case
  76. oldsavename = modelname+'_s_model_cv'+str(cvfold)+'_'+str(oldlr[modelname.split('_')[0]])
  77. state_dict = torch.load(save_loc + oldsavename+'.pth.tar')
  78. #for n, p in state_dict.named_parameters():
  79. # model[n].copy_(p)
  80. model.load_state_dict(state_dict)
  82. #print(nothing)
  83. model.double()
  84. model.to(device)
  85. optimizer = torch.optim.Adam(model.parameters(), lr=lr)
  86. sigmoid = torch.nn.Sigmoid()
  87. train_loss_clean = []
  88. train_loss_corrupt = []
  89. val_losses = []
  90. train_losses = []
  91. update_labels = False
  92. for epoch in range(1, maxiter+1):
  94. epoch_loss = 0.0
  95. train_len = len(train_loader)
  96. val_len = len(val_loader)
  97. val_epoch_loss = 0.0
  98. for batch_idx, data in enumerate(train_loader):
  100. optimizer.zero_grad()
  101. inputs = data['buffers']
  102. det_labels = data['sz_labels'].long().to(device)
  103. b, Nsz, T, _, _ = inputs.shape
  105. inputs = inputs.to(torch.DoubleTensor()).to(device)
  106. k_pred, noise_pred, _ = model(inputs)
  108. y_samples = []
  110. loss = 0.5* detloss(k_pred.reshape(-1, 2), det_labels.reshape(-1)) + 0.5* detloss(noise_pred.reshape(-1, 2), det_labels.reshape(-1))
  112. loss.backward()
  113. optimizer.step()
  115. epoch_loss += loss.item()
  118. for batch_idx, data in enumerate(val_loader):
  119. with torch.no_grad():
  120. optimizer.zero_grad()
  121. inputs = data['buffers']
  122. det_labels = data['sz_labels'].long().to(device)
  123. b, Nsz, T, _, _ = inputs.shape
  125. inputs = inputs.to(torch.DoubleTensor()).to(device)
  126. k_pred, noise_pred, _ = model(inputs)
  128. y_samples = []
  130. loss = 0.5* detloss(k_pred.reshape(-1, 2), det_labels.reshape(-1)) + 0.5* detloss(noise_pred.reshape(-1, 2), det_labels.reshape(-1))
  132. val_epoch_loss += loss.item()
  133. epoch_loss = epoch_loss/train_len
  134. val_epoch_loss = val_epoch_loss/val_len
  135. torch.cuda.empty_cache()
  136. #schedule lr
  138. train_losses.append(epoch_loss)
  139. val_losses.append(val_epoch_loss)
  140. if len(val_losses) > 10 and val_losses[-1] > min(val_losses[-11:-1]):
  141. break
  144. #if epoch_loss <= train_losses[-1] and epoch_val_loss <= val_losses[-1]:
  145. #torch.save(model.state_dict(), '/home/deeksha/EEG_Sz/GenProc/results/lstmAE_'+pt+str(ch_id)+'.pth.tar')
  146. print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f} '.format(epoch, epoch_loss, val_epoch_loss))
  148. torch.save(model.state_dict(), save_loc+savename+ '.pth.tar')
  149. np.save(save_loc+ savename+'_train.npy', train_losses)
  150. np.save(save_loc+savename+'_val.npy', val_losses)
  151. #del model, optimizer, train_loader, validation_loader
  153. return train_losses, val_losses

train_nal.py at commit 3029ac2, no license · at the source

Overview

Authors: Deeksha M Shama1,2, Archana Venkataraman1,2
ORCID iDs: Deeksha M Shama
  1. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
  2. Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts, United States of America
Institutions: Boston University (United States); Johns Hopkins University (United States)
Journal: PloS one, volume 21, issue 6, article e0352191
Dates: received 19 June 2025; accepted 5 June 2026; published online 23 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0352191 · PMID 42335163 · PMCID PMC13289946 · OpenAlex W4404313184
Open access: gold, a free copy (OpenAlex)
Preprint: arxiv:2410.19815
Status: code verified
Categories: EEG (modality), human (organism), epilepsy (population), methods / tools (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Physiology & signal measures
MeSH: Deep Learning*, Electroencephalography*, Seizures*, Algorithms, Bayes Theorem, Humans, Neural Networks, Computer, Uncertainty (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science Foundation (CAREER, 1845430); National Institutes of Health (R21 CA263804, R01 EB029977, CA263804, R01-HD108790, EB029977)
Citations: not cited yet (Europe PMC); 77 references in the paper

Abstract

Deep learning is advancing EEG processing for automated epileptic seizure detection and onset zone localization, yet its performance relies heavily on high-quality annotated training data. However, scalp EEG is susceptible to high noise levels, which in turn leads to imprecise annotations of the seizure timing and characteristics. This “label noise” presents a significant challenge in model training and generalization. In this paper, we introduce Bayesian UncertaiNty-aware Deep Learning (BUNDL), a novel algorithm that informs a deep learning model of label ambiguities, thereby enhancing the robustness of seizure detection systems. By integrating domain knowledge into an underlying Bayesian framework, we derive a novel KL-divergence-based loss function that capitalizes on uncertainty to better learn seizure characteristics from scalp EEG. Thus, BUNDL offers a straightforward and model-agnostic method for training deep neural networks with noisy training labels that does not add any parameters to existing architectures. Additionally, we explore the impact of improved detection system on the task of automated onset zone localization. We validate BUNDL using a comprehensive simulated EEG dataset and two publicly available datasets collected by Temple University Hospital (TUH) and Boston Children’s Hospital (CHB-MIT). Results show that BUNDL consistently identifies noisy labels and improves the robustness of three base models under various label noise conditions. We also conduct ablation experiments on uncertainty quantification, evaluate cross-site generalizability to Siena EEG dataset, and quantify computational cost of all methods. Furthermore, we demonstrate that BUNDL improves seizure onset zone localization accuracy. Ultimately, BUNDL presents as a reliable method that can be seamlessly integrated with existing deep models used in clinical practice, enabling the training of trustworthy models for epilepsy evaluation.

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 5 matches between paragraphs and lines of code.

deeksha-ms/BUNDL

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 3029ac2e1a5d3dfd88529c459e6dca93566c9f02, 3 January 2026
Languages: MATLAB (131), Python (10), Jupyter (2)
Size: 177 files, 143 scripts
Software Heritage: not checked
Found in: the text, “Implementation and evaluation”
Holds: README, documentation, 8 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Tools: EEGLAB (12 files), NumPy (11 files), PyTorch (10 files), pandas (7 files), SciPy (7 files), Matplotlib (4 files), Statistics and Machine Learning Toolbox (3 files), scikit-learn (2 files), FieldTrip (1 file), Parallel Computing Toolbox (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
144 files
At the source: github.com/deeksha-ms/BUNDL

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability

The simulated data can be reproduced using scripts available at https://github.com/deeksha-ms/BUNDL. The TUH dataset is available at https://isip.piconepress.com/projects/tuh_eeg/ the CHB-MIT dataset is available at https://physionet.org/content/chbmit/1.0.0/ and the Siena Dataset is available at https://physionet.org/content/siena-scalp-eeg/1.0.0/. Our code is available at https://github.com/deeksha-ms/BUNDL.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 2, 28 September 2026

  • Funding: added National Science Foundation: CAREER, 1845430; National Institutes of Health: R21 CA263804, R01 EB029977, CA263804, R01-HD108790, EB029977

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 8 MeSH terms, 44 references.

Cite

This paper

M Shama, D., & Venkataraman, A. (2026). Bayesian Uncertainty-aware Deep Learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection. PloS one, 21(6), e0352191. https://doi.org/10.1371/journal.pone.0352191

BibTeX

@article{mshama2026bayesian,
author = {M Shama, Deeksha and Venkataraman, Archana},
title = {{Bayesian Uncertainty-aware Deep Learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection}},
journal = {PloS one},
year = {2026},
month = jun,
volume = {21},
number = {6},
pages = {e0352191},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0352191},
url = {https://doi.org/10.1371/journal.pone.0352191},
pmid = {42335163},
pmcid = {PMC13289946}
}

RIS

TY - JOUR
AU - M Shama, Deeksha
AU - Venkataraman, Archana
TI - Bayesian Uncertainty-aware Deep Learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/06/23
VL - 21
IS - 6
SP - e0352191
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0352191
UR - https://doi.org/10.1371/journal.pone.0352191
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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