Automated signal drift and global fluctuation removal from 4D fMRI data based on principal component analysis as a major preprocessing step for fMRI data analysis.
Harshit S. ParmarBrian NutterL. Rodney LongSameer K. AntaniSunanda MitraPublished in: Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging (2019)
Keyphrases
- preprocessing step
- principal component analysis
- data analysis
- dimensionality reduction
- data preprocessing
- principal components
- independent component analysis
- functional magnetic resonance imaging
- feature extraction
- machine learning
- low dimensional
- dimension reduction
- preprocessing
- face recognition
- high dimensional data
- covariance matrix
- dimensionality reduction methods
- image segmentation and object recognition
- signal processing
- data mining
- linear feature extraction
- feature selection
- linear discriminant analysis
- singular value decomposition
- discriminant analysis
- non stationary
- knowledge discovery
- high dimensional data analysis
- discretization method
- lower dimensional
- feature space
- pattern discovery
- multi aspect
- high dimensional
- data warehouse
- rough sets
- face images
- cluster analysis
- frequency domain