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An explainable multimodal machine learning model for diagnosing disorders of consciousness: evidence from a large multicenter Chinese cohort.

Code ↔ Paper

3 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 3 matches
  1. [1] § Methods › EEG features analysis › Spectral analysis ↔ Preprocessing_autoEEG_pipeline.ipynb, lines 69–153 · score 0.92 · Power spectral density, 8–13 Hz, parieto occipital, 1–4 Hz, 4–8 Hz, Welch
  2. [2] § Methods › EEG data collection and preprocessing ↔ Preprocessing_autoEEG_pipeline.ipynb, lines 69–153 · score 0.85 · eye blink, muscle artifact, ICA components, threshold, Autoreject, epochs
  3. [3] § Methods › EEG data collection and preprocessing ↔ Preprocessing_autoEEG_pipeline.ipynb, lines 22–67 · score 0.65 · notch filter, MNE, Preprocessing, resampled, 0.1 Hz, 40 Hz

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Jupyter notebook · 161 lines · 5.7 KB · no license · 3 matches

  1. # %%
  2. import os
  3. import mne
  4. import numpy as np
  5. import pandas as pd
  6. from scipy.integrate import simpson
  7. from autoreject import AutoReject
  8. from mne.preprocessing import ICA
  9. from mne_icalabel import label_components
  10. from mne_connectivity import spectral_connectivity_epochs
  12. # Folder paths
  13. folder_path = r'F:\eeg_SHAP' # Enter your folder path
  14. save_path = r'F:\eeg_SHAP\process' # Enter your save path
  16. # Get all .vhdr files in the folder
  17. vhdr_files = [f for f in os.listdir(folder_path) if f.endswith('.vhdr')]
  19. # Create an empty DataFrame to store features from all files
  20. all_features = []
  22. # Iterate over all .vhdr files
  23. for vhdr_file in vhdr_files:
  24. file_path = os.path.join(folder_path, vhdr_file)
  25. file_name = os.path.splitext(vhdr_file)[0]
  27. # Load EEG data
  28. raw = mne.io.read_raw_brainvision(file_path, preload=True)
  30. # Apply standard 1005 electrode layout
  31. montage = mne.channels.make_standard_montage('standard_1005')
  32. raw.set_montage(montage)
  34. # Select only EEG channels
  35. raw.pick_types(eeg=True)
  37. # Crop the first 300 seconds of data for processing
  38. raw.crop(tmin=0, tmax=300)
  39. raw.filter(l_freq=0.1, h_freq=40.0, picks="eeg")
  40. raw.notch_filter(freqs=50, notch_widths=4, picks="eeg")
  41. raw.resample(sfreq=500)
  43. # Create pseudo-events by segmenting data into 2-second epochs
  44. interval = 2.0
  45. n_markers = int(raw.times[-1] // interval)
  46. events = mne.make_fixed_length_events(raw, id=200, start=0, stop=n_markers * interval, duration=interval)
  48. # Create epochs object
  49. epochs = mne.Epochs(
  50. raw, events, event_id=200, tmin=0, tmax=interval,
  51. baseline=None, preload=True, detrend=1
  52. )
  54. # Use autoreject to automatically handle bad segments and interpolate bad channels
  55. ar = AutoReject(n_interpolate=[1, 2, 3, 4, 5, 6], random_state=666, n_jobs=-1, verbose=True)
  56. ar.fit(epochs)
  57. epochs_clean, reject_log = ar.transform(epochs, return_log=True)
  59. # ICA preprocessing: filter for training (before fitting ICA)
  60. raw_for_ica = raw.copy()
  61. raw_for_ica.filter(l_freq=1.0, h_freq=40.0)
  63. events_for_ica = mne.make_fixed_length_events(raw_for_ica, id=200, start=0, stop=n_markers * interval, duration=interval)
  64. epochs_for_ica = mne.Epochs(
  65. raw_for_ica, events_for_ica, event_id=200, tmin=0, tmax=interval,
  66. baseline=None, preload=True, detrend=1
  67. )
  69. # Use autoreject cleaned epochs indices
  70. epochs_selection = epochs_clean.selection
  71. filtered_epochs_for_ica = epochs_for_ica[epochs_selection]
  73. # ICA fitting
  74. n_components = min(30, len(filtered_epochs_for_ica.ch_names) - 1)
  75. ica = ICA(n_components=n_components, max_iter="auto", method="picard", random_state=42, fit_params=dict(ortho=False, extended=True))
  76. ica.fit(filtered_epochs_for_ica)
  78. # ICA component labeling
  79. ic_labels = label_components(filtered_epochs_for_ica, ica, method="iclabel")
  80. labels = ic_labels["labels"]
  81. y_pred_proba = ic_labels["y_pred_proba"]
  83. # Set artifact types and thresholds
  84. artifact_types = {
  85. "eye blink": 0.8,
  86. "heart beat": 0.8,
  87. "muscle artifact": 0.8
  88. }
  90. exclude_idx = []
  91. for idx, (label, proba_array) in enumerate(zip(labels, y_pred_proba)):
  92. if label in artifact_types and artifact_types[label] <= np.max(proba_array):
  93. exclude_idx.append(idx)
  95. # Apply ICA to remove artifact components
  96. ica.apply(epochs_clean, exclude=exclude_idx)
  98. # Remove extreme value epochs
  99. reject_criteria = dict(eeg=180e-6)
  100. epochs_clean.drop_bad(reject=reject_criteria)
  102. # Apply average reference
  103. epochs_clean.set_eeg_reference(ref_channels='average')
  105. # Randomly select 30 epochs
  106. np.random.seed(42)
  107. max_epochs = 30
  108. selected_indices = np.random.choice(len(epochs_clean), size=max_epochs, replace=False)
  109. epochs_ica = epochs_clean[selected_indices]
  111. # Power spectral analysis - Parieto-occipital alpha relative, delta absolute, alpha/theta ratio
  112. region = ['Oz', 'O1', 'O2', 'POz', 'PO3', 'PO4', 'PO7', 'PO8', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P8']
  113. available = [ch for ch in region if ch in epochs_ica.info['ch_names']]
  114. psd = epochs_ica.compute_psd(method='welch', fmin=1, fmax=40, picks=available)
  115. freqs = psd.freqs
  116. psds = psd.get_data().mean(axis=0).mean(axis=0)
  117. freq_res = np.mean(np.diff(freqs))
  118. total = simpson(psds, dx=freq_res)
  120. def band_power(low, high):
  121. idx = np.logical_and(freqs >= low, freqs <= high)
  122. return simpson(psds[idx], dx=freq_res)
  124. alpha = band_power(8, 13)
  125. delta = band_power(1, 4)
  126. theta = band_power(4, 8)
  127. rel_alpha = alpha / total
  128. alpha_theta_ratio = alpha / theta
  130. # Calculate IMCOH theta
  131. con_theta = spectral_connectivity_epochs(
  132. epochs_ica, method='imcoh', mode='multitaper', sfreq=epochs_ica.info['sfreq'],
  133. fmin=4, fmax=8, faverage=True, tmin=0.0, mt_adaptive=False, n_jobs=-1
  134. ).get_data()
  135. imcoh_theta = np.mean(np.abs(con_theta))
  137. # Calculate PLV beta
  138. con_beta = spectral_connectivity_epochs(
  139. epochs_ica, method='plv', mode='multitaper', sfreq=epochs_ica.info['sfreq'],
  140. fmin=13, fmax=30, faverage=True, tmin=0.0, mt_adaptive=False, n_jobs=-1
  141. ).get_data()
  142. plv_beta = np.mean(np.abs(con_beta))
  144. # Save the features for each file
  145. features = {
  146. 'File': file_name,
  147. 'PO_alpha_rel': rel_alpha,
  148. 'PO_delta_abs': delta * 1e12,
  149. 'PO_alpha_theta_ratio': alpha_theta_ratio,
  150. 'PLV_beta': plv_beta,
  151. 'IMCOH_theta': imcoh_theta
  152. }
  153. all_features.append(features)
  155. # Save features from all files as a CSV
  156. df_all = pd.DataFrame(all_features)
  157. output_csv = os.path.join(save_path, 'EEG_features.csv')
  158. df_all.to_csv(output_csv, index=False)
  160. print(f"All features saved to: {output_csv}")

Preprocessing_autoEEG_pipeline.ipynb at commit 5d47c46, no license · at the source

Overview

Authors: Chenye Mou1, Jiajia Zhao1, Zilong Yan2, Li Zhang1,3, Xuejiao Tian4, Yingchen Wang1, Hanxiao Wang1, Jun Hu1, Zhuolin He1, Yi Ling1, Anshun Kang2, Qiwen Luo2, Jian Gao5, Xiangming Ye3, Lirong Hong6, Jingqi Li5, Tianyi Yan2, Jie Yu1, Benyan Luo1
ORCID iDs: Benyan Luo
  1. Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang China
  2. School of Medical Technology, Beijing Institute of Technology, Beijing, China
  3. Centre for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang China
  4. Department of Neurology, Shaoxing Shangyu Second People’s Hospital, Shaoxing, Zhejiang China
  5. Department of Rehabilitation, Hangzhou Mingzhou Brain Rehabilitation Hospital, Hangzhou, Zhejiang China
  6. Department of Rehabilitation, Hangzhou Hospital of Zhejiang Armed Police Corps, Hangzhou, Zhejiang China
Institutions: First Affiliated Hospital Zhejiang University (China); Zhejiang University (China); Beijing Institute of Technology (China); Zhejiang Provincial People's Hospital (China); Hangzhou Medical College (China); Shaoxing Second Hospital (China); Chinese People's Armed Police Force
Journal: Journal of translational medicine, volume 24, issue 1, article 708
Dates: received 26 February 2026; accepted 2 April 2026; published online 15 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s12967-026-08108-y · PMID 41987185 · PMCID PMC13217752 · OpenAlex W7154470854
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), other condition (population), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Physiology & signal measures
Keywords: Disorders of consciousness, Intensive care, Machine learning, Diagnosis, SHAP analysis
MeSH: Consciousness Disorders*, Machine Learning*, Algorithms, China, Cohort Studies, East Asian People, Electroencephalography, Humans, Predictive Learning Models, Reproducibility of Results, ROC Curve (* major topic)
Topic: Traumatic Brain Injury Research (Epidemiology, Medicine), according to OpenAlex
Funding: the National Natural Science Foundation of China (8250050433, U22A20293); The Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project (2021ZD0200404); the China Postdoctoral Science Foundation (2024M752878); the China Brain Project (2022ZD0208905); Zhejiang Provincial Natural Science Foundation of China (QN25H090042)
Citations: cited by 2 papers (Europe PMC); 36 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

Vongoleo/docmodel

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 5d47c46dec3b8bf113a1c267047df5e0cf805aef, 1 October 2025
Languages: R (1), Jupyter (1)
Size: 7 files, 2 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: autoreject (1 file), caret (1 file), ggplot2 (1 file), ICLabel (1 file), MNE-Python (1 file), MNE-Connectivity (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
3 files
At the source: github.com/Vongoleo/docmodel

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

Tracing map

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  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Code and data availability statement

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  • it points to the authors' code: Vongoleo/docmodel
  • it says that the data are available on request

Read it in the paper: doi.org/10.1186/s12967-026-08108-y.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 19 authors, 5 keywords, 11 MeSH terms, 5 funders, 33 references.

Cite

This paper

Mou, C., Zhao, J., Yan, Z., Zhang, L., Tian, X., Wang, Y., Wang, H., Hu, J., He, Z., Ling, Y., Kang, A., Luo, Q., Gao, J., Ye, X., Hong, L., Li, J., Yan, T., Yu, J., & Luo, B. (2026). An explainable multimodal machine learning model for diagnosing disorders of consciousness: evidence from a large multicenter Chinese cohort. Journal of translational medicine, 24(1), 708. https://doi.org/10.1186/s12967-026-08108-y

BibTeX

@article{mou2026explainable,
author = {Mou, Chenye and Zhao, Jiajia and Yan, Zilong and Zhang, Li and Tian, Xuejiao and Wang, Yingchen and Wang, Hanxiao and Hu, Jun and He, Zhuolin and Ling, Yi and Kang, Anshun and Luo, Qiwen and Gao, Jian and Ye, Xiangming and Hong, Lirong and Li, Jingqi and Yan, Tianyi and Yu, Jie and Luo, Benyan},
title = {{An explainable multimodal machine learning model for diagnosing disorders of consciousness: evidence from a large multicenter Chinese cohort}},
journal = {Journal of translational medicine},
year = {2026},
month = apr,
volume = {24},
number = {1},
pages = {708},
publisher = {BMC},
issn = {1479-5876},
doi = {10.1186/s12967-026-08108-y},
url = {https://doi.org/10.1186/s12967-026-08108-y},
pmid = {41987185},
pmcid = {PMC13217752}
}

RIS

TY - JOUR
AU - Mou, Chenye
AU - Zhao, Jiajia
AU - Yan, Zilong
AU - Zhang, Li
AU - Tian, Xuejiao
AU - Wang, Yingchen
AU - Wang, Hanxiao
AU - Hu, Jun
AU - He, Zhuolin
AU - Ling, Yi
AU - Kang, Anshun
AU - Luo, Qiwen
AU - Gao, Jian
AU - Ye, Xiangming
AU - Hong, Lirong
AU - Li, Jingqi
AU - Yan, Tianyi
AU - Yu, Jie
AU - Luo, Benyan
TI - An explainable multimodal machine learning model for diagnosing disorders of consciousness: evidence from a large multicenter Chinese cohort
T2 - Journal of translational medicine
J2 - J Transl Med
PY - 2026
DA - 2026/04/15
VL - 24
IS - 1
SP - 708
SN - 1479-5876
PB - BMC
DO - 10.1186/s12967-026-08108-y
UR - https://doi.org/10.1186/s12967-026-08108-y
LA - en
ER -

CSL-JSON

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