003: CW_ICA: An Efficient Dimensionality Selection Method for Independent Component Analysis

Independent component analysis (ICA) is one of the most commonly used blind source separation (BSS) techniques for signal pre-processing. The performance of the ICA results depends on the preset number of independent components (ICs). Too many ICs leads to under-decomposition of mixed signals, whereas too few ICs results in overfitting of source signals. In this study, we propose a novel multivariate method to determine the optimal number of ICs, named the column-wise independent component analysis (CW_ICA). It measures the relationship between ICs from two different blocks by the smallest of column-wise maximum value in off-diagonal rank-based correlation matrix to automatically identify the optimal number of ICs. With simulation and raw scalp EEG signal data as validation set, we compare the proposed CW_ICA to several existing methods combined different ICA methods. Results show that the proposed CW_ICA is a reliable and robust method for determining the optimal number of components in ICA. This method is robust, has broad applicability (i.e., EEG, LC-MS, etc.) and can be used in conjunction with a variety of ICA methods (i.e., FastICA, Infomax, etc.).

Independent component analysis

Optimal number

Column-wise

Correlation coefficient

Robust

Automatically 

Implementation and Analysis

Conference on Statistical Practice (CSP) 2023