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Speech Recognition with an fMRISNN Constrained by Human Functional Brain Networks: A Study of Enhanced MFCC-Driven Sparse Spike Encoding.

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Paper

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

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The authors' code

MATLAB · 22 lines · 665 B · no license

  1. function f=enframe(x,win,inc)
  2. nx=length(x(:)); %xΪÓïÒôÐźŠwinΪ´° incΪ֡³¤»ò´°º¯Êý
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  6. if (nwin == 1)
  7. len = win;
  8. else
  9. len = nwin;
  10. end
  11. if (nargin < 3)
  12. inc = len;
  13. end
  14. nf = fix((nx-len+inc)/inc);
  15. f=zeros(nf,len);
  16. indf= inc*(0:(nf-1)).';
  17. inds = (1:len);
  18. f(:) = x(indf(:,ones(1,len))+inds(ones(nf,1),:));
  19. if (nwin > 1)
  20. w = win(:)';
  21. f = f .* w(ones(nf,1),:);
  22. end

enframe.m at commit 84c1c71, no license · at the source

Overview

Authors: Lei Guo1,2, Nancheng Ma1, Zhuoxuan Wang1, Rumeng Liu1
ORCID iDs: Lei Guo
  1. Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; (N.M.); (Z.W.); (R.L.)
  2. State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei University of Technology, Tianjin 300401, China
Institutions: Hebei University of Technology (China)
Journal: Biomimetics (Basel, Switzerland), volume 11, issue 5, article 302
Dates: received 10 March 2026; accepted 23 April 2026; published online 26 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/biomimetics11050302 · PMID 42187369 · PMCID PMC13204488 · OpenAlex W7156773704
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Graphs, Complexity, Machine learning, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: SNN, fMRI, functional brain network, sparse spike encoding, speech recognition
Topic: Neural Networks and Reservoir Computing (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: National Natural Science Foundation of China (52477232)
Citations: not cited yet (Europe PMC); 45 references in the paper

Abstract

Spiking neural networks (SNNs) offer inherent advantages in processing temporal information. However, their network topologies are predominantly algorithm-generated, lacking constraints from biological brain connectivity, which limits their bio-plausibility. In our previous work, we constructed a spiking neural network (SNN) by incorporating the topological structure of functional brain networks derived from fMRI data of healthy subjects and proposed an fMRISNN model. This model was further employed as the reservoir layer of a liquid state machine (LSM) to build a speech recognition framework. In this framework, the Lyon ear model and the BSA were used to encode speech signals into spike sequences; however, this approach suffers from high computational cost and limited adaptability to temporal variations. To address these limitations, we propose an enhanced Mel-frequency cepstral coefficient (MFCC)-driven sparse spike encoding method. For the speech recognition task, we systematically compare the two preprocessing pipelines in terms of spike number, spike sparsity, encoding time, and downstream speech recognition performance. Experimental results show that the proposed method generates substantially fewer spikes, achieves markedly higher sparsity, and requires significantly less encoding time, while maintaining nearly the same recognition accuracy under the same LSM-based framework. These findings indicate that improved speech input representation can enhance the computational efficiency of SNN-based speech recognition without compromising recognition capability. In addition, the fMRISNN model significantly outperforms several baseline models with algorithmically generated topologies. Compared with mainstream models reported in the literature, although the deep convolutional neural network (CNN) still achieves higher absolute recognition accuracy, the fMRISNN exhibits clear advantages in terms of model parameter size and theoretical energy efficiency.

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

Repository

Its files are read in the Code ↔ Paper reader above.

syhtsr/fMRI-SNN

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 84c1c71a57586b57e4738a6cf778f8fc22748ccf, 9 March 2024
Languages: MATLAB (11)
Size: 12 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: Signal Processing Toolbox (1 file)
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/syhtsr/fMRI-SNN.git

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;
  • no match between paragraphs and code yet;
  • 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 original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. Complete code reproducing simulation results written in MATLAB is publicly available at https://github.com/syhtsr/fMRI-SNN.git (accessed on 22 April 2026).

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 1 funder, 34 references.

Cite

This paper

Guo, L., Ma, N., Wang, Z., & Liu, R. (2026). Speech Recognition with an fMRISNN Constrained by Human Functional Brain Networks: A Study of Enhanced MFCC-Driven Sparse Spike Encoding. Biomimetics (Basel, Switzerland), 11(5), 302. https://doi.org/10.3390/biomimetics11050302

BibTeX

@article{guo2026speech,
author = {Guo, Lei and Ma, Nancheng and Wang, Zhuoxuan and Liu, Rumeng},
title = {{Speech Recognition with an fMRISNN Constrained by Human Functional Brain Networks: A Study of Enhanced MFCC-Driven Sparse Spike Encoding}},
journal = {Biomimetics (Basel, Switzerland)},
year = {2026},
month = apr,
volume = {11},
number = {5},
pages = {302},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2313-7673},
doi = {10.3390/biomimetics11050302},
url = {https://doi.org/10.3390/biomimetics11050302},
pmid = {42187369},
pmcid = {PMC13204488}
}

RIS

TY - JOUR
AU - Guo, Lei
AU - Ma, Nancheng
AU - Wang, Zhuoxuan
AU - Liu, Rumeng
TI - Speech Recognition with an fMRISNN Constrained by Human Functional Brain Networks: A Study of Enhanced MFCC-Driven Sparse Spike Encoding
T2 - Biomimetics (Basel, Switzerland)
J2 - Biomimetics (Basel)
PY - 2026
DA - 2026/04/26
VL - 11
IS - 5
SP - 302
SN - 2313-7673
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/biomimetics11050302
UR - https://doi.org/10.3390/biomimetics11050302
LA - en
ER -

CSL-JSON

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