Separation of the global and local components in functional near-infrared spectroscopy signals using principal component spatial filtering
2016
Global systemic effects not specific to a task can be prominent in functional near-infrared spectroscopy (fNIRS) signals and the separation of task-specific fNIRS signals and global nonspecific effects is challenging due to waveform correlations. We describe a principal component spatial filter algorithm for separation of the global and local effects. The effectiveness of the approach is demonstrated using fNIRS signals acquired during a right finger-thumb tapping task where the response patterns are well established. Both the temporal waveforms and the spatial pattern consistencies between oxyhemoglobin and deoxyhemoglobin signals are significantly improved, consistent with the basic physiological basis of fNIRS signals and the expected pattern of activity associated with the task.
Keywords:
- Principal component analysis
- Spectroscopy
- Common spatial pattern
- Computer vision
- Waveform
- Computer science
- Artificial intelligence
- Data mining
- Spatial filter
- Functional near-infrared spectroscopy
- Text mining
- Data acquisition
- Ecology
- Convolution
- Near-infrared spectroscopy
- Pattern recognition
- Functional magnetic resonance imaging
- Biology
- Correction
- Source
- Cite
- Save
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