Name
Abstract | ||
|---|---|---|
Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical functional neuroimaging that has seen rapid development and increasing use in studying human brain under normal and diseased conditions. Compared with bloodoxygenation-les el dependent functional magnetic resonance imaging (BOLD fMRI), fNIRS offers advantages including its low cost, portability and compatibility with implanted medical devices. Thus, INIRS can be used to monitor brain activity particularly in infants, elders and patients who are unable to undergo routine IMRI scans. However, fNIRS suffers from its susceptibility to scalp and to systemic physiological noises. Fluctuations originated from heartbeat, respiration and low frequency oscillations lead to contamination of cerebral activity. In order to tap the full potential of fNIRS, it is essential to eliminate these confounding noises from fNIRS measurements. Therefore, the present study aims to understand the underlying relationship between superficial signals and the compound signals respectis ely measured by short channels and long channels of fNIRS optodes in a whole head configuration. Our results reveal that: 1) 49.56% of total variances in long-channel data are contributed by a global component shared across all long channels; 2) this global component is significantly correlated with the superficial fluctuations extracted from short-channel data. Finally, our findings indicate that compound signals measured by long channels of fNIRS are contaminated by superficial fluctuations and that careful removal of these fluctuations from long channel data is critical in obtaining accurate images of cerebral activity with fNIRS. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8856349 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Biomedical engineering,Computer vision,Heartbeat,Functional magnetic resonance imaging,Functional neuroimaging,Computer science,Brain activity and meditation,Functional near-infrared spectroscopy,Human brain,Artificial intelligence | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Zhang | 1 | 0 | 1.35 |
| Daniel Cheong | 2 | 0 | 0.68 |
| Yuxuan Chen | 3 | 5 | 8.88 |
| Ali Khan | 4 | 0 | 0.34 |
| Lei Ding | 5 | 142 | 26.77 |
| Han Yuan | 6 | 0 | 0.34 |