Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis

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

doi:10.1109/PAEE59932.2023.10244522

Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis

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

Machine Learning

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

3 Citations (Scopus)

Abstract

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 language	English
Title of host publication	2023 Progress in Applied Electrical Engineering, PAEE 2023
Publisher	IEEE
Publication date	2023
Pages	1-5
ISBN (Electronic)	9798350316254
DOIs	https://doi.org/10.1109/PAEE59932.2023.10244522
Publication status	Published - 2023
Event	2023 Progress in Applied Electrical Engineering, PAEE 2023 - Koscielisko, Poland Duration: 26 Jun 2023 → 30 Jun 2023

Conference

Conference	2023 Progress in Applied Electrical Engineering, PAEE 2023
Country/Territory	Poland
City	Koscielisko
Period	26/06/2023 → 30/06/2023
Sponsor	Polish 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.

Keywords

cascade filtering
functional near-infrared spectroscopy
Machine Learning
signal processing

Access to Document

10.1109/PAEE59932.2023.10244522

Cite this

Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis. / Pelc, Mariusz; Mikolajewski, Dariusz; Ruotsalo, Tuukka; Leiva, Luis A.; Sudol, Adam; Gorzelanczyk, Edward Jacek; Lysiak, Adam; Kawala-Sterniuk, Aleksandra.

2023 Progress in Applied Electrical Engineering, PAEE 2023. IEEE, 2023. p. 1-5.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Pelc, M, Mikolajewski, D, Ruotsalo, T, Leiva, LA, Sudol, A, Gorzelanczyk, EJ, Lysiak, A & Kawala-Sterniuk, A 2023, Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis. in 2023 Progress in Applied Electrical Engineering, PAEE 2023. IEEE, pp. 1-5, 2023 Progress in Applied Electrical Engineering, PAEE 2023, Koscielisko, Poland, 26/06/2023. https://doi.org/10.1109/PAEE59932.2023.10244522

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abstract = "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. ",

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author = "Mariusz Pelc and Dariusz Mikolajewski and Tuukka Ruotsalo and Leiva, {Luis A.} and Adam Sudol and Gorzelanczyk, {Edward Jacek} and Adam Lysiak and Aleksandra Kawala-Sterniuk",

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