TensorCSBP: A Tensor Center-Symmetric Feature Extractor for EEG Odor Detection.
Overview
- Department of Neurology, School of Medicine, Firat University, Elazig 23119, Turkey
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey
- School of Business (Information System), University of Southern Queensland, Toowoomba 4350, Australia
- Department of Computer Engineering, College of Engineering, Erzurum Technical University, Erzurum 25050, Turkey
- Vocational School of Technical Sciences, Firat University, Elazig 23119, Turkey
Abstract
Objective: Accurate odor classification from EEG signals requires informative and interpretable features. Although Local Binary Pattern (LBP) and variants such as the center-symmetric binary pattern are widely used, they lack sufficient explainability and tensor-level implementations. Additionally, neuroscientific understanding of odor processing remains limited. Methods: We propose Tensor Center-Symmetric Binary Pattern (TensorCSBP), a novel tensor-based feature extractor designed for EEG odor analysis. TensorCSBP is integrated into an explainable feature engineering (XFE) pipeline with four steps: (1) TensorCSBP for feature generation, (2) CWNCA for feature selection, (3) tkNN classifier for decision making, and (4) DLob method for symbolic interpretability. Results: TensorCSBP XFE was evaluated on a newly collected 32-channel EEG dataset for odor detection. It achieved 96.68% accuracy under 10-fold cross-validation. Conclusions: The information entropy of the DLob symbol sequence was 3.5675, demonstrating the richness of the interpretability output. Significance: This study presents a high-accuracy, explainable, and computationally efficient model for EEG-based odor classification. TensorCSBP bridges low-level signal patterns with symbolic neuroscience insights, offering real-time potential for BCI and clinical applications.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
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Data
Datasets cited
- kaggle.com/
datasets/ , at Kaggle; found in “Data Availability Statement”buraktaci
Data Availability Statement
The dataset can be downloaded at https://
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, 8 authors, 5 keywords, 2 funders, 57 references.
Cite
This paper
Tasci, I., Sercek, I., Talu, Y., Barua, P. D., Baygin, M., Tasci, B., Dogan, S., & Tuncer, T. (2026). TensorCSBP: A Tensor Center-Symmetric Feature Extractor for EEG Odor Detection. Diagnostics (Basel, Switzerland), 16(5), 789. https://
BibTeX
@article{tasci2026tensor
author = {Tasci, Irem and Sercek, Ilknur and Talu, Yunus and Barua, Prabal Datta and Baygin, Mehmet and Tasci, Burak and Dogan, Sengul and Tuncer, Turker},
title = {{TensorCSBP: A Tensor Center-Symmetric Feature Extractor for EEG Odor Detection}},
journal = {Diagnostics (Basel, Switzerland)},
year = {2026},
month = mar,
volume = {16},
number = {5},
pages = {789},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2075-4418},
doi = {10.3390/
url = {https://
pmid = {41828065},
pmcid = {PMC12984240}
}
RIS
TY - JOUR
AU - Tasci, Irem
AU - Sercek, Ilknur
AU - Talu, Yunus
AU - Barua, Prabal Datta
AU - Baygin, Mehmet
AU - Tasci, Burak
AU - Dogan, Sengul
AU - Tuncer, Turker
TI - TensorCSBP: A Tensor Center-Symmetric Feature Extractor for EEG Odor Detection
T2 - Diagnostics (Basel, Switzerland)
J2 - Diagnostics (Basel)
PY - 2026
DA - 2026/
VL - 16
IS - 5
SP - 789
SN - 2075-4418
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/
UR - https://
LA - en
ER -
CSL-JSON
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"author": [
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"language": "en",
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