Functional near-infrared spectroscopy (fNIRS) is a neuroimaging modality that measures the hemodynamic responses to brain activation. With its cost-effectiveness and portability, fNIRS can be utilized to measure brain signals in the everyday world. However, factors such as blood pressure, cardiac rhythms, and motion can obscure the hemodynamic response function (HRF) obtained in fNIRS data. Motion, in particular, poses a significant challenge in obtaining the HRF for measurements conducted in everyday world activities when the subject is free to move.
To address this, the General Linear Model (GLM) with temporally embedded Canonical Correlation Analysis (tCCA) has been shown to be effective in extracting the HRF by reducing motion and other systemic interferences. Recently, deep learning methods have also demonstrated its potential for time-series data analysis. The objective of this project is to evaluate the effectiveness of a novel transformer-based deep learning approach in comparison to the tradition method of GLM with tCCA
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/48881 |
| Date | 24 May 2024 |
| Creators | Serani, Teah |
| Contributors | Cheng, Xiaojun |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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