@ARTICLE{Pelc_Mariusz_Machine_Early,
 author={Pelc, Mariusz and Mikolajewski, Dariusz and Luckiewicz, Adrian and Sudol, Adam and Mendon, Patryk and Gorzelańczyk, Edward Jacek and Kawala-Sterniuk, Aleksandra},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e152605},
 howpublished={online},
 year={Early Access},
 abstract={This paper presents a preliminary study delving into the application of machine learning-based methods for optimising 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 non-invasive 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.},
 type={Article},
 title={Machine Learning-based Filtering system for fNIRS Signals’ Analysis Purpose},
 URL={http://czasopisma.pan.pl/Content/133049/PDF/BPASTS-04483-EA.pdf},
 doi={10.24425/bpasts.2024.152605},
 keywords={functional near-infrared spectroscopy, biomedical signal processing, machine learning; filtering, brain signals, smoothing filtering, cascade filtering},
}