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EEG Biomarkers for Affective Disorders Diagnosis: An Evaluation and Validation Study.

Code ↔ Paper

6 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 6 matches
  1. [1] § The Biomarkers › Time and Frequency Domain › Power Spectral Density (PSD) ↔ get_segment.py, lines 31–58 · score 0.80 · 0.5–4 Hz, 30–50 Hz, frequency bands, 4–8 Hz, delta, power
  2. [2] § Empirical Validation and Analysis › Evaluation of Biomarker Effectiveness ↔ extract_features_examples.py, lines 50–111 · score 0.78 · detrended fluctuation, SamEn, approximate entropy, sample entropy, AppEn, DFA
  3. [3] § Empirical Validation and Analysis › Dataset and Preprocessing › B‐SNIP 1 Dataset ↔ process.py, lines 11–28 · score 0.68 · notch filter, bad channels, M1, M2, 0.1 Hz, 50 Hz
  4. [4] § Empirical Validation and Analysis › Analysis of Time‐Frequency Biomarkers › Power Spectral Density (PSD) ↔ get_segment.py, lines 31–58 · score 0.67 · Power spectral density, frequency bands, delta, beta, theta, alpha
  5. [5] § Empirical Validation and Analysis › Analysis of Complexity‐Related Biomarkers ↔ extract_features_examples.py, lines 50–111 · score 0.59 · Higuchi fractal dimension, detrended fluctuation, DFA, HFD
  6. [6] § The Biomarkers › Time and Frequency Domain › Power Spectral Density (PSD) ↔ extract_features_examples.py, lines 1–47 · score 0.53 · 30–50 Hz, 4–8 Hz, beta, theta, alpha, 30 Hz

Paper

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The authors' code

Python · 226 lines · 9.2 KB · no license · 3 matches

  1. import os
  2. import scipy.io as sio
  3. from main_preprocessing import Preprocessing
  4. import mne
  5. import pandas as pd
  6. import lmdb
  7. import pickle
  8. import numpy as np
  9. import antropy as ant
  10. from mi import compute_mi
  11. from statsmodels.tsa.stattools import grangercausalitytests
  12. from tqdm import tqdm
  13. from connectivity import plv_connectivity,pli_connectivity,ccf_connectivity,coh_connectivity,icoh_connectivity
  14. from mne_connectivity import spectral_connectivity_epochs
  15. from torcheeg import transforms
  16. from extract_feature import extract_connectivity,data_append
  17. from lds import smooth_feature
  18. import warnings
  19. import mne_microstates
  20. from mne_connectivity import spectral_connectivity_epochs,vector_auto_regression
  21. warnings.filterwarnings('ignore')
  22. # %%
  23. root_dir = '/data0/cyn/DEAP/data_preprocessed_python'
  24. files = [file for file in os.listdir(root_dir)]
  25. files = sorted(files,key=lambda x: (int(x[1:3])))
  28. files_dict = {
  29. 'train':files[:32]
  30. # 'val':files[20:21],
  31. # 'test':files[20:21],
  32. }
  35. eeg_duration = 1
  36. baseline_duration = 3 # 秒
  38. all_fre ={"theta": [4, 8],
  39. "alpha": [8, 14],
  40. "beta": [14, 30],
  41. "gama": [30, 50]}
  43. t1 = transforms.BandSampleEntropy(band_dict=all_fre,sampling_rate=128,R=0.2)
  44. t2 = transforms.BandHiguchiFractalDimension(band_dict=all_fre,sampling_rate=128)
  45. t3 = transforms.BandHjorth(band_dict=all_fre,sampling_rate=128,mode='mobility')
  46. t4 = transforms.BandHjorth(band_dict=all_fre,sampling_rate=128,mode='complexity')
  47. t = transforms.BandDetrendedFluctuationAnalysis(band_dict=all_fre,sampling_rate=128)
  50. ######################################################################
  51. # extract features method
  52. ######################################################################
  53. # "AppEn":transforms.BandApproximateEntropy(band_dict=all_fre,sampling_rate=128),
  54. # "SamEn":transforms.BandSampleEntropy(band_dict=all_fre,sampling_rate=128,R=0.2),
  55. # "HFD":transforms.BandHiguchiFractalDimension(band_dict=all_fre,sampling_rate=128),
  56. # "DFA":transforms.BandDetrendedFluctuationAnalysis(band_dict=all_fre,sampling_rate=128),
  57. # "hjc":transforms.BandHjorth(band_dict=all_fre,sampling_rate=128,mode='complexity'),
  58. # "hjm":transforms.BandHjorth(band_dict=all_fre,sampling_rate=128,mode='mobility'),
  59. # "DE":transforms.BandDifferentialEntropy(band_dict=all_fre),
  60. # "micro_state":mne_microstates.segment(one, n_states=4,
  61. # random_state=0,
  62. # return_polarity=True),
  63. # "GC":compute_gc_matrix,
  64. # "PLV":extract_connectivity(data,"plv"),
  65. # "PLI":extract_connectivity(data,"pli"),
  66. # "cohy":spectral_connectivity_epochs(data=sample1,method='cohy',sfreq=128,fmin=4,fmax=50,mode='multitaper',faverage=True),
  67. # "imcoh":spectral_connectivity_epochs(data=sample1,method='imcoh',sfreq=128,fmin=4,fmax=50,mode='multitaper',faverage=True),
  68. # "dpli":spectral_connectivity_epochs(data=sample1,method='dpli',sfreq=128,fmin=4,fmax=50,mode='multitaper',faverage=True),
  69. # "SpecEn":transforms.BandSampleEntropy(band_dict=all_fre,sampling_rate=128),
  70. # "kur":transforms.BandKurtosis(),
  71. # "ske":transforms.BandSkewness(),
  72. # "PSD":mne.time_frequency.psd_array_multitaper(sample,128,fmin=4,fmax=50),
  73. # "PermEn":ant.perm_entropy(),
  74. # "MI":get_MI(),
  75. # "PFD":transforms.BandPetrosianFractalDimension(band_dict=all_fre,sampling_rate=128),
  76. # "SVDEn":transforms.BandSVDEntropy(band_dict=all_fre,sampling_rate=128),
  77. # "CCF":extract_connectivity(data,"ccf"),,
  78. # "coh":spectral_connectivity_epochs(data=sample1,method='coh',sfreq=128,fmin=4,fmax=50,mode='multitaper',faverage=True)
  79. def compute_gc_matrix(data, maxlag=2, verbose=False):
  80. """
  81. 计算 Granger 因果矩阵 (num_channels x num_channels)
  83. 参数:
  84. - data: ndarray, shape (num_channels, n_times)
  85. - maxlag: int, VAR模型的最大滞后阶数
  86. - verbose: 是否打印grangercausalitytests的详细结果
  88. 返回:
  89. - gc_matrix: ndarray, shape (num_channels, num_channels)
  90. gc_matrix[i, j] 表示 通道i → 通道j 的GC强度
  91. """
  92. num_channels, n_times = data.shape
  93. gc_matrix = np.zeros((num_channels, num_channels))
  95. for i in range(num_channels):
  96. for j in range(num_channels):
  97. if i == j:
  98. continue # 自因果设为0
  99. # 构造输入格式 (T, 2)，第0列是被预测的序列，第1列是潜在的因果序列
  100. test_data = np.vstack([data[j], data[i]]).T
  102. try:
  103. results = grangercausalitytests(test_data, maxlag=maxlag, verbose=verbose)
  104. # 提取某个指标作为 GC 强度，这里用 maxlag 阶的 F 检验的 p-value
  105. p_value = results[maxlag][0]['ssr_ftest'][1]
  106. gc_matrix[i, j] = -np.log(p_value + 1e-10) # p 越小，因果越强（取负log方便可视化）
  107. except Exception as e:
  108. print(f"GC计算失败: {i}->{j}, 错误: {e}")
  109. gc_matrix[i, j] = 0
  111. return gc_matrix
  113. def get_MI(data):
  114. target=[]
  115. for i in range(40):
  116. target.append([0 for i in range(40)])
  117. for i in range(40):
  118. for j in range(i,40):
  119. mi = compute_mi(data[i], data[j],20)
  120. target[i][j]=mi
  121. target[j][i]=mi
  122. return np.array(target)
  124. all_de=[]
  125. all_plv=[]
  126. all_lds_de=[]
  127. all_lds_plv=[]
  128. all_label=[]
  129. all_data=[]
  130. num=0
  131. # path = '/data0/violin/projects/szbd_practice/szbd_dataset_eeg_64_noICA'
  132. # files = os.listdir(path)
  133. # dict_num = {}
  134. # for file in files:
  135. # s = file.split('-')
  136. # if s[-3]!='RestEyesOpen' and s[-3]!='RestEyesClosed':
  137. # continue
  138. # name = s[-3]+"_"+s[-2]
  139. # if not name in dict_num.keys():
  140. # dict_num[name]=1
  141. # else:
  142. # dict_num[name]+=1
  143. # print(dict_num)
  144. # assert(1==0)
  145. for files_key in files_dict.keys():
  146. for file in tqdm(files_dict[files_key]):
  147. # num+=1
  148. # if num<=9:
  149. # continue
  150. # print(files_dict[files_key])
  151. # assert(1==0)
  152. # print(file[:-4])
  153. # assert(1==0)
  154. data_path = os.path.join(root_dir, file)
  155. data_label = pickle.load(open(data_path, 'rb'), encoding='latin1')
  156. data = data_label['data']
  157. data = data.reshape(40,40,63,128)
  158. data = data.transpose(0, 2, 1, 3)
  159. label_valence = data_label['labels']
  160. baseline_values = np.mean(data[:, :baseline_duration, :, :], axis=1) # shape (40, 40, 128)
  161. data = data - baseline_values[:, np.newaxis, :, :]
  162. data = data[:,baseline_duration:]
  163. # data = data.transpose(0,2,1,3)
  164. # print(data.shape)
  165. # assert(1==0)
  166. sub_de=[]
  167. sub_plv=[]
  168. de_lds=[]
  169. plv_lds=[]
  170. sub_label=[]
  171. sub_data=[]
  172. for i in tqdm(range(data.shape[0])):
  173. samples = data[i]
  174. # if label_valence[i]>5:
  175. # label_valence[i]=0
  176. # else:
  177. # label_valence[i]=1
  178. session_de=[]
  179. session_plv=[]
  180. session_label=[]
  181. session_data=[]
  182. # print(data.shape[1],'fffffffff')
  183. # assert(1==0)
  184. for j in range(data.shape[1] // eeg_duration):
  185. sample = samples[eeg_duration * j:eeg_duration * (j + 1)][0]
  186. maps = []
  187. for x1 in sample:
  188. tem = ant.perm_entropy(x1, normalize=True)
  189. maps.append(tem)
  190. maps = np.array(maps)
  191. ske = np.expand_dims(maps,axis=-1)
  192. # print(maps.shape)
  193. # assert(1==0)
  194. # plv = extract_connectivity(sample,'pli')
  195. session_de=data_append(session_de,ske)
  196. # session_plv=data_append(session_plv,maps3)
  197. # session_label=data_append(session_label,label_valence[i])
  198. # session_data=data_append(session_data,maps2)
  199. # tem_plv=LDS_plv(session_plv)
  200. # tem_de=session_de
  201. # tem_de=tem_de.transpose(0, 2, 1)
  202. # tem_de=smooth_feature(tem_de)
  203. # tem_de=tem_de.transpose(0, 2, 1)
  204. sub_de=data_append(sub_de,session_de)
  205. # sub_plv=data_append(sub_plv,session_plv)
  206. # de_lds=data_append(de_lds,tem_de)
  207. # plv_lds=data_append(plv_lds,tem_plv)
  208. # sub_label=data_append(sub_label,session_label)
  209. # sub_data=data_append(sub_data,session_data)
  210. # print(sub_de.shape,de_lds.shape)
  211. # assert(1==0)
  212. # all_lds_de=data_append(all_lds_de,de_lds)
  213. # all_lds_plv=data_append(all_lds_plv,plv_lds)
  214. # np.save('/data0/lyt/emotion/seed/DEAP/gc_{}.npy'.format(file[:-4]),all_de)
  216. all_de=data_append(all_de,sub_de)
  217. # all_plv=data_append(all_plv,sub_plv)
  218. # all_label=data_append(all_label,sub_label)
  219. # all_data=data_append(all_data,sub_data)
  220. # np.save('/data0/lyt/emotion/seed/DEAP/plvLDS.npy',all_lds_plv)
  221. # np.save('/data0/lyt/emotion/seed/DEAP/deLDS4band.npy',all_lds_de)
  222. # np.save('/data0/lyt/emotion/seed/DEAP/dpli.npy',all_plv)
  223. np.save('/data0/lyt/emotion/seed/DEAP/permen.npy',all_de)
  224. # np.save('/data0/lyt/emotion/seed/DEAP/label.npy',all_label)
  225. # np.save('/data0/lyt/emotion/seed/DEAP/imcoh.npy',all_data)
  226. print(all_de.shape)

extract_features_examples.py at commit e33abd2, no license · at the source

Overview

Authors: Sha Zhao1,2, Yitian Liu1,2, Jiquan Wang1,2, Ruizhe Zhang1,2, Yilin Wang1,2, Haiteng Jiang1,3,4, Shijian Li1,2, Tao Li1,3,4, Gang Pan1,2,3
ORCID iDs: Shijian Li, Tao Li
  1. State Key Laboratory of Brain‐Machine Intelligence, Zhejiang University, Hangzhou, China
  2. College of Computer Science and Technology, Zhejiang University, Hangzhou, China
  3. MOE Frontier Science Center for Brain Science and Brain‐Machine Integration, Zhejiang University, Hangzhou, China
  4. Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
Institutions: Zhejiang University (China); Hangzhou Seventh Peoples Hospital (China)
Journal: Human brain mapping, volume 47, issue 13, article e70628
Dates: received 20 June 2025; accepted 9 August 2026; published online 6 September 2026; in print September 2026
Type: Review · Language: English
License: CC BY-NC
Identifiers: DOI 10.1002/hbm.70628 · PMID 42702788 · PMCID PMC13547600 · OpenAlex W7211897563
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), depression (population), schizophrenia / psychosis (population), bipolar (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Machine learning, Complexity, Graphs, Physiology & signal measures
Keywords: affective disorders diagnosis, biomarker, EEG
MeSH: Bipolar Disorder*, Electroencephalography*, Major Depressive Disorder*, Mood Disorders*, Psychotic Disorders*, Schizophrenia*, Adult, Biomarkers, Female, Humans, Male, Middle Aged, Young Adult (* major topic)
Topic: Emotion and Mood Recognition (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: STI 2030-Major Projects (2021ZD0200400); the Natural Science Foundation of Zhejiang Province, China (LZ24F020004); National Natural Science Foundation of China (62476240)
Citations: not cited yet (Europe PMC); 116 references in the paper

Abstract

Electroencephalography (EEG) provides real‐time, dynamic insights into brain function, making it a valuable tool for the screening, diagnosis, and treatment of affective disorders. The use of EEG‐derived features as biomarkers for affective disorder diagnosis has attracted growing attention. In this work, we introduce 25 commonly used EEG features and categorize them into four domains: time‐domain, frequency‐domain, complexity, and connectivity. Each feature captures distinct aspects of emotional processing and serves as a potential biomarker for diagnosing affective disorders. To evaluate their diagnostic utility, we analyzed two independent resting‐state EEG datasets. The first dataset comprised 84 healthy controls, 62 patients with schizophrenia (SZ), 43 patients with schizoaffective disorder (SAD), and 32 patients with bipolar disorder (BD), while the second included 28 healthy controls and 32 patients with major depressive disorder (MDD). These features are extracted and used for classification, allowing us to identify biomarkers with strong discriminative power. Finally, we provide a comprehensive discussion on key issues in the field, including multimodal biomarker integration, challenges in EEG‐based diagnosis, variability in classification results, and factors influencing EEG feature extraction.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

yt-liu88/EEGBiomarkers-for-Affective-Disorders-Diagnosis

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: e33abd2c6713fc3fd3a4e2fe94f0fe705d260d1a, 2 September 2025
Languages: Python (4)
Size: 5 files, 4 scripts
Software Heritage: not archived
Found in: the text, “Dataset and Preprocessing”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (4 files), MNE-Python (3 files), Matplotlib (2 files), PyTorch (2 files), ICLabel (1 file), MNE-Connectivity (1 file), pandas (1 file), PyWavelets (1 file), scikit-learn (1 file), SciPy (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
5 files
At the source: github.com/yt-liu88/EEGBiomarkers-for-Affective-Disorders-Diagnosis

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

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.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 4 scripts, each with its path and the digest of its content;
  • 6 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

No dataset and no data link were found in the paper.

Data Availability Statement

The data that support the findings of this study are openly available in National Institute of Mental Health (NIMH) Data Archive at https://nda.nih.gov/edit_collection.html?id=2274. Additional supporting information may be found in the online version of the article at the publisher's website. The source code has been made publicly available and can be accessed via the following GitHub repository: https://github.com/yt‐liu88/EEGBiomarkers‐for‐Affective‐Disorders‐Diagnosis (https://github.com/yt-liu88/EEGBiomarkers-for-Affective-Disorders-Diagnosis).

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

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 3 keywords, 13 MeSH terms, 3 funders, 91 references.

Cite

This paper

Zhao, S., Liu, Y., Wang, J., Zhang, R., Wang, Y., Jiang, H., Li, S., Li, T., & Pan, G. (2026). EEG Biomarkers for Affective Disorders Diagnosis: An Evaluation and Validation Study. Human brain mapping, 47(13), e70628. https://doi.org/10.1002/hbm.70628

BibTeX

@article{zhao2026eeg,
author = {Zhao, Sha and Liu, Yitian and Wang, Jiquan and Zhang, Ruizhe and Wang, Yilin and Jiang, Haiteng and Li, Shijian and Li, Tao and Pan, Gang},
title = {{EEG Biomarkers for Affective Disorders Diagnosis: An Evaluation and Validation Study}},
journal = {Human brain mapping},
year = {2026},
month = sep,
volume = {47},
number = {13},
pages = {e70628},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70628},
url = {https://doi.org/10.1002/hbm.70628},
pmid = {42702788},
pmcid = {PMC13547600}
}

RIS

TY - JOUR
AU - Zhao, Sha
AU - Liu, Yitian
AU - Wang, Jiquan
AU - Zhang, Ruizhe
AU - Wang, Yilin
AU - Jiang, Haiteng
AU - Li, Shijian
AU - Li, Tao
AU - Pan, Gang
TI - EEG Biomarkers for Affective Disorders Diagnosis: An Evaluation and Validation Study
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/09/01
VL - 47
IS - 13
SP - e70628
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70628
UR - https://doi.org/10.1002/hbm.70628
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "EEG Biomarkers for Affective Disorders Diagnosis: An Evaluation and Validation Study",
"container-title": "Human brain mapping",
"author": [
{
"family": "Zhao",
"given": "Sha"
},
{
"family": "Liu",
"given": "Yitian"
},
{
"family": "Wang",
"given": "Jiquan"
},
{
"family": "Zhang",
"given": "Ruizhe"
},
{
"family": "Wang",
"given": "Yilin"
},
{
"family": "Jiang",
"given": "Haiteng"
},
{
"family": "Li",
"given": "Shijian"
},
{
"family": "Li",
"given": "Tao"
},
{
"family": "Pan",
"given": "Gang"
}
],
"container-title-short": "Hum Brain Mapp",
"volume": "47",
"issue": "13",
"page": "e70628",
"DOI": "10.1002/hbm.70628",
"PMID": "42702788",
"PMCID": "PMC13547600",
"ISSN": "1065-9471",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/hbm.70628",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
1
]
]
}
}

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