Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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 | |
| 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 |
|---|---|
| Land | Poland |
| By | Koscielisko |
| Periode | 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.
- cascade filtering, functional near-infrared spectroscopy, Machine Learning, signal processing
Research areas
ID: 383791215