Standard
Improving fNIRS Signal Quality Using Smoothing Filtering. / Kawala-Sterniuk, Aleksandra; Mikolajewski, Dariusz; Leiva, Luis A.; Ruotsalo, Tuukka; Lysiak, Adam; Grochowicz, Barbara; Jacek Gorzenariczyk, Edward; Luckiewicz, Adrian; Wieczorek, Anna; Pelc, Mariusz.
2023 Progress in Applied Electrical Engineering, PAEE 2023. IEEE, 2023. p. 1-8.
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Kawala-Sterniuk, A, Mikolajewski, D, Leiva, LA
, Ruotsalo, T, Lysiak, A, Grochowicz, B, Jacek Gorzenariczyk, E, Luckiewicz, A, Wieczorek, A & Pelc, M 2023,
Improving fNIRS Signal Quality Using Smoothing Filtering. in
2023 Progress in Applied Electrical Engineering, PAEE 2023. IEEE, pp. 1-8, 2023 Progress in Applied Electrical Engineering, PAEE 2023, Koscielisko, Poland,
26/06/2023.
https://doi.org/10.1109/PAEE59932.2023.10244393APA
Kawala-Sterniuk, A., Mikolajewski, D., Leiva, L. A.
, Ruotsalo, T., Lysiak, A., Grochowicz, B., Jacek Gorzenariczyk, E., Luckiewicz, A., Wieczorek, A., & Pelc, M. (2023).
Improving fNIRS Signal Quality Using Smoothing Filtering. In
2023 Progress in Applied Electrical Engineering, PAEE 2023 (pp. 1-8). IEEE.
https://doi.org/10.1109/PAEE59932.2023.10244393Vancouver
Kawala-Sterniuk A, Mikolajewski D, Leiva LA
, Ruotsalo T, Lysiak A, Grochowicz B et al.
Improving fNIRS Signal Quality Using Smoothing Filtering. In 2023 Progress in Applied Electrical Engineering, PAEE 2023. IEEE. 2023. p. 1-8
https://doi.org/10.1109/PAEE59932.2023.10244393Author
Kawala-Sterniuk, Aleksandra ; Mikolajewski, Dariusz ; Leiva, Luis A. ; Ruotsalo, Tuukka ; Lysiak, Adam ; Grochowicz, Barbara ; Jacek Gorzenariczyk, Edward ; Luckiewicz, Adrian ; Wieczorek, Anna ; Pelc, Mariusz. / Improving fNIRS Signal Quality Using Smoothing Filtering. 2023 Progress in Applied Electrical Engineering, PAEE 2023. IEEE, 2023. pp. 1-8
Bibtex
@inproceedings{783ca04f2d7341c1a35116e8647907fb,
title = "Improving fNIRS Signal Quality Using Smoothing Filtering",
abstract = "Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared. ",
keywords = "brain signals, filtering, functional near-infrared spectroscopy, preprocessing, smoothing filtering",
author = "Aleksandra Kawala-Sterniuk and Dariusz Mikolajewski and Leiva, {Luis A.} and Tuukka Ruotsalo and Adam Lysiak and Barbara Grochowicz and {Jacek Gorzenariczyk}, Edward and Adrian Luckiewicz and Anna Wieczorek and Mariusz Pelc",
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/PAEE59932.2023.10244393",
language = "English",
pages = "1--8",
booktitle = "2023 Progress in Applied Electrical Engineering, PAEE 2023",
publisher = "IEEE",
}
RIS
TY - GEN
T1 - Improving fNIRS Signal Quality Using Smoothing Filtering
AU - Kawala-Sterniuk, Aleksandra
AU - Mikolajewski, Dariusz
AU - Leiva, Luis A.
AU - Ruotsalo, Tuukka
AU - Lysiak, Adam
AU - Grochowicz, Barbara
AU - Jacek Gorzenariczyk, Edward
AU - Luckiewicz, Adrian
AU - Wieczorek, Anna
AU - Pelc, Mariusz
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared.
AB - Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared.
KW - brain signals
KW - filtering
KW - functional near-infrared spectroscopy
KW - preprocessing
KW - smoothing filtering
U2 - 10.1109/PAEE59932.2023.10244393
DO - 10.1109/PAEE59932.2023.10244393
M3 - Article in proceedings
AN - SCOPUS:85174243826
SP - 1
EP - 8
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 -
