Unsupervised Dynamic Time Warping Clustering for Robust Functional Network Identification in fNIRS Motor Tasks.

Overview

Authors: Murad Althobaiti¹

ORCID iDs: Murad Althobaiti

Biomedical Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia

Institutions: Imam Abdulrahman Bin Faisal University (Saudi Arabia)

Journal: Sensors (Basel, Switzerland), volume 26, issue 6, article 1848

Dates: received 30 January 2026; accepted 13 March 2026; published online 15 March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.3390/s26061848 · PMID 41902016 · PMCID PMC13030481 · OpenAlex W7136401564

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: fNIRS (modality), human (organism), systems (subfield)

Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, Connectivity, fMRI & imaging

Keywords: brain-computer interface, Dynamic Time Warping, fNIRS, functional connectivity, motor cortex

MeSH: Brain-Computer Interfaces*, Clustering Algorithms*, Algorithms, Cluster Analysis, Humans, Signal Processing, Computer-Assisted, Spectroscopy, Near-Infrared (* major topic)

Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Citations: not cited yet (Europe PMC); 33 references in the paper

Abstract

Functional near-infrared spectroscopy (fNIRS) is a valuable non-invasive modality for brain-computer interfaces (BCIs), but robust signal interpretation is challenged by the significant temporal variability of the hemodynamic response. Standard linear methods, such as Pearson correlation, often fail to capture functional connectivity when signals exhibit temporal jitter. This study validates an unsupervised Dynamic Time Warping (DTW) clustering framework to robustly identify motor networks from fNIRS data by accommodating non-linear temporal shifts. We analyzed a public fNIRS dataset (N = 30) across right-hand (RHT), left-hand (LHT), and foot tapping (FT) tasks. A robust preprocessing pipeline was implemented, including Wavelet Motion Correction and Common Average Referencing (CAR) to remove artifacts and global systemic noise. The core method involved computing Z-score normalized DTW distance matrices, followed by hierarchical clustering. To validate the framework, we benchmarked it against a standard Pearson Correlation method. Results show that the unsupervised DTW framework achieved a network identification accuracy of 53.17%, significantly outperforming the standard Pearson correlation benchmark (48.06%) with a statistically significant difference (p < 0.05). The framework successfully detected distinct, somatotopically correct modulations: superior-medial activation during foot tapping and lateralized activation during hand tapping. These findings demonstrate that unsupervised DTW clustering is a robust, data-driven approach that outperforms conventional linear methods in capturing functional networks during motor tasks, showing significant potential for next-generation asynchronous BCIs.

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

Code

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Data

Datasets cited

figshare:9783755, at figshare; found in “Data Availability Statement”

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: Figshare, https://doi.org/10.6084/m9.figshare.9783755.v1 (accessed on 7 March 2026).

Reproduced under the paper's license (CC BY), 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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 1 author, 5 keywords, 7 MeSH terms, 32 references.

Cite

This paper

Althobaiti, M. (2026). Unsupervised Dynamic Time Warping Clustering for Robust Functional Network Identification in fNIRS Motor Tasks. Sensors (Basel, Switzerland), 26(6), 1848. https://doi.org/10.3390/s26061848

BibTeX

@article{althobaiti2026unsupervised, author = {Althobaiti, Murad}, title = {{Unsupervised Dynamic Time Warping Clustering for Robust Functional Network Identification in fNIRS Motor Tasks}}, journal = {Sensors (Basel, Switzerland)}, year = {2026}, month = mar, volume = {26}, number = {6}, pages = {1848}, publisher = {Multidisciplinary Digital Publishing Institute (MDPI)}, issn = {1424-8220}, doi = {10.3390/s26061848}, url = {https://doi.org/10.3390/s26061848}, pmid = {41902016}, pmcid = {PMC13030481} }

RIS

TY - JOUR AU - Althobaiti, Murad TI - Unsupervised Dynamic Time Warping Clustering for Robust Functional Network Identification in fNIRS Motor Tasks T2 - Sensors (Basel, Switzerland) J2 - Sensors (Basel) PY - 2026 DA - 2026/03/15 VL - 26 IS - 6 SP - 1848 SN - 1424-8220 PB - Multidisciplinary Digital Publishing Institute (MDPI) DO - 10.3390/s26061848 UR - https://doi.org/10.3390/s26061848 LA - en ER -

CSL-JSON

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