Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  • Mariusz Pelc
  • Dariusz Mikolajewski
  • Ruotsalo, Tuukka
  • Luis A. Leiva
  • Adam Sudol
  • Edward Jacek Gorzelanczyk
  • Adam Lysiak
  • Aleksandra Kawala-Sterniuk

This paper presents a preliminary study on the use of machine learning-based methods to select the appropriate parameters of cascade filters in the analysis of brain signals recorded using functional infrared spectroscopy (fNIRS), which shows the level of oxygenation in the brain and, unlike EEG signals (showing electrical brain activity), are less prone to potential interference, disturbances or artifacts occurrence.

Original languageEnglish
Title of host publication2023 Progress in Applied Electrical Engineering, PAEE 2023
Publication date2023
ISBN (Electronic)9798350316254
Publication statusPublished - 2023
Event2023 Progress in Applied Electrical Engineering, PAEE 2023 - Koscielisko, Poland
Duration: 26 Jun 202330 Jun 2023


Conference2023 Progress in Applied Electrical Engineering, PAEE 2023
SponsorPolish Society of Theoretical and Applied Electrical Engineering (PTETiS), The Institute of Electrical and Electronics Engineers (IEEE), Warsaw University of Technology

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

    Research areas

  • cascade filtering, functional near-infrared spectroscopy, Machine Learning, signal processing

ID: 383791215