Standard
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
Harvard
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/PAEE59932.2023.10244522APA
Pelc, M., Mikolajewski, D.
, Ruotsalo, T., Leiva, L. A., Sudol, A., Gorzelanczyk, E. J., 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 (pp. 1-5). IEEE.
https://doi.org/10.1109/PAEE59932.2023.10244522Vancouver
Pelc M, Mikolajewski D
, Ruotsalo T, Leiva LA, Sudol A, Gorzelanczyk EJ et al.
Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis. In 2023 Progress in Applied Electrical Engineering, PAEE 2023. IEEE. 2023. p. 1-5
https://doi.org/10.1109/PAEE59932.2023.10244522Author
Pelc, Mariusz ; Mikolajewski, Dariusz ; Ruotsalo, Tuukka ; Leiva, Luis A. ; Sudol, Adam ; Gorzelanczyk, Edward Jacek ; Lysiak, Adam ; Kawala-Sterniuk, Aleksandra. / Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis. 2023 Progress in Applied Electrical Engineering, PAEE 2023. IEEE, 2023. pp. 1-5
Bibtex
@inproceedings{87ef8dd6db69466c9f227237a0af772f,
title = "Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis",
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. ",
keywords = "cascade filtering, functional near-infrared spectroscopy, Machine Learning, signal processing",
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",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 Progress in Applied Electrical Engineering, PAEE 2023 ; Conference date: 26-06-2023 Through 30-06-2023",
year = "2023",
doi = "10.1109/PAEE59932.2023.10244522",
language = "English",
pages = "1--5",
booktitle = "2023 Progress in Applied Electrical Engineering, PAEE 2023",
publisher = "IEEE",
}
RIS
TY - GEN
T1 - Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis
AU - Pelc, Mariusz
AU - Mikolajewski, Dariusz
AU - Ruotsalo, Tuukka
AU - Leiva, Luis A.
AU - Sudol, Adam
AU - Gorzelanczyk, Edward Jacek
AU - Lysiak, Adam
AU - Kawala-Sterniuk, Aleksandra
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - cascade filtering
KW - functional near-infrared spectroscopy
KW - Machine Learning
KW - signal processing
U2 - 10.1109/PAEE59932.2023.10244522
DO - 10.1109/PAEE59932.2023.10244522
M3 - Article in proceedings
AN - SCOPUS:85174266658
SP - 1
EP - 5
BT - 2023 Progress in Applied Electrical Engineering, PAEE 2023
PB - IEEE
T2 - 2023 Progress in Applied Electrical Engineering, PAEE 2023
Y2 - 26 June 2023 through 30 June 2023
ER -
