Physics augmented classification of fNIRS signals

Felipe Orihuela-Espina, Michelle Rojas-Cisneros, Samuel A. Montero-Hernández, Jesús S. García-Salinas, Bibiana Cuervo-Soto, Javier Herrera-Vega

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


Background. Predictive classification favors performance over semantics. In traditional predictive classification pipelines, feature engineering is often oblivious to the underlying phenomena. Hypothesis. In applied domains, such as functional near infrared spectroscopy (fNIRS), the exploitation of physical knowledge may improve the discriminative quality of our observation set. Aim. Give exemplary evidence that intervening the physical observation process can augment classification. Methods. We manipulate the observation process in four ways independently. First, sampling and quantization are designed to enhance class-related contrast. Second, we show how selection of optical filters affects the cross-talk, in turn, affecting classification. Third, we regularize the inverse problem to maximize sensitivity to any gradient that would later support the classification. And fourth, we introduce a catalyst covariate during experiment design to exacerbate response differences. Results. For each of the proposed manipulations, we show that the performance of the classification exercise is altered in some way or another. Conclusions. Exploitation knowledge of physics even before acquisition can support classification, alleviating otherwise blind feature engineering. This can also enhance interpretability of the classification model.

Original languageEnglish
Title of host publicationBiosignal Processing and Classification Using Computational Learning and Intelligence
Subtitle of host publicationPrinciples, Algorithms, and Applications
Number of pages31
ISBN (Electronic)9780128201251
Publication statusPublished - 14 Jan 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Inc. All rights reserved.


  • Functional optical neuroimages (fNIRS)
  • Neuroimaging
  • Physics-based classification

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)


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