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  3. Tensorcsbp: A Tensor Center-symmetric Feature Extractor For Eeg Odor Detection.
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  3. Tensorcsbp: A Tensor Center-symmetric Feature Extractor For Eeg Odor Detection.

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TensorCSBP: A Tensor Center-Symmetric Feature Extractor for EEG Odor Detection.

Irem Tasci1, Ilknur Sercek2, Yunus Talu2

  • 1Department of Neurology, School of Medicine, Firat University, Elazig 23119, Turkey.

Diagnostics (Basel, Switzerland)
|March 14, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed a new method, Tensor Center-Symmetric Binary Pattern (TensorCSBP), for classifying odors from EEG signals. This explainable approach achieves high accuracy, offering potential for brain-computer interfaces and clinical use.

Keywords:
Directed LobishEEG odor detectionEEG signal classificationTensorCSBPexplainable feature engineering

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Accurate odor classification from electroencephalography (EEG) signals is crucial but limited by feature explainability and tensor-level implementations.
  • Existing methods like Local Binary Pattern (LBP) lack sufficient interpretability for neuroscientific insights into odor processing.

Purpose of the Study:

  • To introduce Tensor Center-Symmetric Binary Pattern (TensorCSBP) as a novel tensor-based feature extractor for EEG odor analysis.
  • To develop an explainable feature engineering (XFE) pipeline for enhanced EEG-based odor classification.

Main Methods:

  • The proposed TensorCSBP method was integrated into a four-step XFE pipeline: TensorCSBP feature generation, CWNCA feature selection, tkNN classification, and DLob symbolic interpretability.
  • The pipeline was evaluated on a new 32-channel EEG dataset specifically collected for odor detection tasks.

Main Results:

  • The TensorCSBP XFE pipeline achieved a high accuracy of 96.68% on the odor detection task, validated through 10-fold cross-validation.
  • The information entropy of the DLob symbol sequence was calculated to be 3.5675, indicating a rich interpretability output.

Conclusions:

  • This study presents a highly accurate, explainable, and computationally efficient model for EEG-based odor classification.
  • TensorCSBP effectively bridges low-level signal patterns with symbolic neuroscience insights, demonstrating potential for real-time Brain-Computer Interface (BCI) and clinical applications.