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Alzheimer's Disease Detection Using Combined EEG Source Connectivity and Microstate Features.

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  1. [1] § 2. Materials and Methods › 2.4. Weighted Phase Lag Index (wPLI) ↔ extract_source_wPLI.m, the whole file · a weak match · score 0.78 · upper triangular, 8–13 Hz, EEG signals, symmetric, alpha, Lag
  2. [2] § 2. Materials and Methods › 2.4. Weighted Phase Lag Index (wPLI) ↔ extract_source_wPLI.m, the whole file · a weak match · score 0.75 · cross spectrum, Phase Lag, formula, weighted, preprocessing, PLI
  3. [3] § 2. Materials and Methods › 2.5. Microstate ↔ extract_microstate_features.m, the whole file · a weak match · score 0.66 · global field power, GFP, ratio, clustering, Microstate, signal
  4. [4] § 2. Materials and Methods › 2.6. Feature Selection ↔ classification.m, the whole file · a weak match · score 0.54 · feature selection, ReliefF, ranks, weight, fold, training
  5. [5] § 2. Materials and Methods ↔ extract_microstate_features.m, the whole file · a weak match · score 0.51 · preprocessed EEG signals, clustering, wPLI, segmentation, microstates, temporal

Paper

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

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The authors' code

MATLAB · 49 lines · 1.9 KB · no license · 2 matches

  1. function source_wpli_features = extract_source_wPLI(eeg_data, fs, leadfield_matrix)
  2. % EXTRACT_SOURCE_WPLI Computes source-space wPLI connectivity features.
  3. %
  4. % Inputs:
  5. % eeg_data - [n_channels x n_samples] preprocessed EEG signal
  6. % fs - Sampling frequency (Hz)
  7. % leadfield_matrix - Leadfield/head model matrix for source estimation
  8. %
  9. % Output:
  10. % source_wpli_features - Vector of averaged source-level wPLI values
  12. %% 1. Source Localization (e.g., sLORETA / eLORETA transformation)
  13. % Compute inverse solution operator (pseudo-inverse approach for demonstration)
  14. inv_op = pinv(leadfield_matrix);
  15. source_signals = inv_op * eeg_data; % [n_sources x n_samples]
  17. %% 2. Bandpass Filter for Alpha Band (8 - 13 Hz)
  18. f_low = 8; f_high = 13;
  19. source_alpha = bandpass(source_signals', [f_low f_high], fs)';
  21. %% 3. Calculate Weighted Phase Lag Index (wPLI)
  22. n_sources = size(source_alpha, 1);
  24. % Compute analytic signals using Hilbert Transform
  25. analytic_signals = hilbert(source_alpha')'; % Complex signals
  27. wpli_matrix = zeros(n_sources, n_sources);
  29. for i = 1:n_sources - 1
  30. for j = i + 1:n_sources
  31. % Cross-spectrum imag part
  32. cross_spec_imag = imag(analytic_signals(i, :) .* conj(analytic_signals(j, :)));
  34. % wPLI formula: |E{Im(X)}| / E{|Im(X)|}
  35. numerator = abs(mean(cross_spec_imag));
  36. denominator = mean(abs(cross_spec_imag));
  38. if denominator ~= 0
  39. wpli_matrix(i, j) = numerator / denominator;
  40. wpli_matrix(j, i) = wpli_matrix(i, j); % Symmetric matrix
  41. end
  42. end
  43. end
  45. %% 4. Extract Connectivity Features (Upper triangular / Regional Averages)
  46. % Extract upper triangle values excluding diagonal
  47. mask = triu(true(n_sources), 1);
  48. source_wpli_features = wpli_matrix(mask)';
  49. end

extract_source_wPLI.m at commit b7752f2, no license · at the source

Overview

Authors: Lu Huang1, Zheng Hu2, Zhengnan Zhang1, Yunyuan Gao2
ORCID iDs: Yunyuan Gao
  1. HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310005, China; (L.H.); (Z.Z.)
  2. School of Automation, Hangzhou Dianzi University, Hangzhou 310005, China
Institutions: Hangzhou Dianzi University (China)
Journal: Brain sciences, volume 16, issue 8, article 856
Dates: received 3 July 2026; accepted 10 August 2026; published online 13 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/brainsci16080856 · PMID 42651165 · PMCID PMC13511316 · OpenAlex W7202347593
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), Alzheimer's / dementia (population)
Methods: Spectral & time-frequency, Machine learning, Smoothing, state filtering, decompositions, Statistics, Preprocessing, Evoked potentials, Connectivity, fMRI & imaging
Keywords: Alzheimer’s disease (AD), EEG source localization (ESL), weighted phase lag index (wPLI), microstate
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Zhejiang Province Natural Science Foundation (LZ25F010006); National Natural Science Foundation of China (62371171, 62371178, 62271181)
Citations: not cited yet (Europe PMC); 50 references in the paper

Abstract

Highlights: A multi-domain feature fusion algorithm (SMMS) is proposed for EEG-based Alzheimer’s disease detection, combining source connectivity and microstate features. A unified microstate template is introduced to standardize analysis, enabling robust feature extraction and enhanced diagnostic performance under strict subject-independent validation. Alpha-band functional connectivity and microstate parameters are identified as exploratory candidate features for AD assessment.

Abstract: Background/Objectives: Electroencephalography (EEG) connectivity and microstate analysis have shown great potential for Alzheimer’s disease (AD) diagnosis; however, their clinical application remains limited by the low spatial resolution of EEG and the lack of standardized microstate analysis. To address these challenges, this study proposes a multi-domain feature fusion framework, namely Source-localized Microstate and Multi-frequency Synchronization (SMMS), which integrates EEG source localization (ESL)-based weighted phase lag index (wPLI) functional connectivity with EEG microstate features. Methods: Specifically, ESL was employed to improve the spatial resolution of EEG signals for constructing functional connectivity matrices, while multi-frequency-band wPLI features were extracted to characterize functional synchronization among cortical regions. Meanwhile, EEG microstate features were utilized to capture the temporal dynamics of brain functional states. The proposed framework was evaluated on a public OpenNeuro dataset comprising 36 AD patients, 23 frontotemporal dementia (FTD) patients, and 29 healthy controls (HCs), as well as an additional clinical dataset collected from 48 AD patients at Sir Run Run Shaw Hospital, Hangzhou, China. Results: Experimental results showed that the proposed SMMS framework achieved high classification performance on both datasets. Conclusions: These findings demonstrate its effectiveness for EEG-based Alzheimer’s disease diagnosis.

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.

7788890/SMMS-AD-Code

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b7752f236b2153bfab5461aa168ef3a5b8cc31b0, 4 August 2026
Languages: MATLAB (11)
Size: 15 files, 11 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (6 files), Signal Processing Toolbox (4 files), EEGLAB (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
12 files
At the source: github.com/7788890/SMMS-AD-Code

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;
  • 11 scripts, each with its path and the digest of its content;
  • 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

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

Data Availability Statement

The code used in this study is publicly available at [https://github.com/7788890/SMMS-AD-Code (accessed on 2 April 2026)]. The EEG datasets analyzed in this study are available from OpenNeuro (Dataset I, https://openneuro.org/) and the corresponding author upon reasonable request (Dataset II).

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, issue, pages, dates, 4 authors, 4 keywords, 2 funders, 48 references.

Cite

This paper

Huang, L., Hu, Z., Zhang, Z., & Gao, Y. (2026). Alzheimer's Disease Detection Using Combined EEG Source Connectivity and Microstate Features. Brain sciences, 16(8), 856. https://doi.org/10.3390/brainsci16080856

BibTeX

@article{huang2026alzheimer,
author = {Huang, Lu and Hu, Zheng and Zhang, Zhengnan and Gao, Yunyuan},
title = {{Alzheimer's Disease Detection Using Combined EEG Source Connectivity and Microstate Features}},
journal = {Brain sciences},
year = {2026},
month = aug,
volume = {16},
number = {8},
pages = {856},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2076-3425},
doi = {10.3390/brainsci16080856},
url = {https://doi.org/10.3390/brainsci16080856},
pmid = {42651165},
pmcid = {PMC13511316}
}

RIS

TY - JOUR
AU - Huang, Lu
AU - Hu, Zheng
AU - Zhang, Zhengnan
AU - Gao, Yunyuan
TI - Alzheimer's Disease Detection Using Combined EEG Source Connectivity and Microstate Features
T2 - Brain sciences
J2 - Brain Sci
PY - 2026
DA - 2026/08/13
VL - 16
IS - 8
SP - 856
SN - 2076-3425
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/brainsci16080856
UR - https://doi.org/10.3390/brainsci16080856
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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