Machine learning-based filtering system for fNIRS signals analysis purpose

• Autorzy: Pelc Mariusz , Mikolajewski Dariusz , Luckiewicz Adrian , Sudol Adam , Mendon Patryk , Gorzelańczyk Edward Jacek , Kawala-Sterniuk Aleksandra

Identyfikatory

DOI

10.24425/bpasts.2024.152605

• Języki publikacji: EN

Abstrakty

EN

This paper presents a preliminary study delving into the application of machine learning-based methods for optimizing parameter selection in filtering techniques. The authors focus on exploring the efficacy of two prominent filtering methods: smoothing and cascade filters, known for their profound impact on enhancing the quality of brain signals. The study specifically examines signals acquired through functional near-infrared spectroscopy (fNIRS), a noninvasive neuroimaging modality offering valuable insights into brain activity. Through meticulous analysis, the research underscores the potential of machine learning approaches in discerning optimal parameters for filtering, thereby leading to a significant enhancement in the quality and reliability of fNIRS-derived signals. The results demonstrate the effectiveness of machine learning-based methods in optimizing parameter selection for filtering techniques, particularly in the context of fNIRS signals. By leveraging these approaches, the study achieves notable improvements in the quality and reliability of brain signal data. This work sheds light on promising avenues for refining neuroimaging methodologies and advancing the field of signal processing in neuroscience. The successful application of machine learning-based techniques highlights their potential for optimizing neuroimaging data processing, ultimately contributing to a deeper understanding of brain function.

Słowa kluczowe

EN

biomedical signal processing; machine learning; filtering; functional near infrared spectroscopy; brain signal; smoothing filter; cascade filtration

PL

funkcjonalna spektroskopia bliskiej podczerwieni; przetwarzanie sygnałów biomedycznych; uczenie maszynowe; filtrowanie; sygnał elektryczny mózgu; filtry kaskadowe; filtr wygładzający

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2025
• Tom: Vol. 73, nr 1
• Strony: art. no. e152605
• Opis fizyczny: Bibliogr. 52 poz., rys., wykr.

Twórcy

autor

Pelc Mariusz

• Institute of Computer Science, University of Opole, Opole, Poland
• School of Computing and Mathematical Sciences, University of Greenwich, London, UK

• https://orcid.org/0000-0003-2818-1010

autor

Mikolajewski Dariusz

• Institute of Computer Science, Kazimierz Wielki University in Bydgoszcz, Bydgoszcz, Poland
• Neuropsychological Research Unit, 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation, Medical University in Lublin, Lublin, Poland

• https://orcid.org/0000-0003-4157-2796

autor

Luckiewicz Adrian

• Faculty of Electrical Engineering, Institute of Theory of Electrical Engineering, Measurement and Information Systems, Warsaw University of Technology, Warszawa, Poland

• https://orcid.org/0000-0001-7771-4827

autor

Sudol Adam

• University of Applied Sciences in Nysa, Department of Technical Sciences, Nysa, Poland

• https://orcid.org/0000-0001-9620-0688

autor

Mendon Patryk

• Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, Poland

• https://orcid.org/0000-0002-5484-1855

autor

Gorzelańczyk Edward Jacek

• Kazimierz Wielki University in Bydgoszcz, Institute of Philosophy, Bydgoszcz, Poland
• The Society for the Substitution Treatment of Addiction "Medically Assisted Recovery", 85-791 Bydgoszcz, Poland

• https://orcid.org/0000-0001-9334-9700

autor

Kawala-Sterniuk Aleksandra

• Department of Artificial Intelligence, Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wroclaw, Poland

• https://orcid.org/0000-0001-7826-1292

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).