Independent Component Analysis Combined with Principal Component Analysis

In EEG signal processing, the combination of Independent Component Analysis (ICA) and Principal Component Analysis (PCA) proves to be an exceptionally effective methodology. Specifically, this approach employs ICA algorithms (such as FastICA or Infomax) to decompose EEG signals into multiple independent components, each representing distinct neural activities. Technically, ICA operates by maximizing statistical independence between components, often using non-Gaussianity measures through functions like kurtosis or negentropy optimization. Subsequently, PCA is applied to these independent components for dimensionality reduction, typically by computing eigenvectors and eigenvalues of the covariance matrix to identify principal components retaining maximum variance. This PCA stage simplifies the component space while preserving essential information, enabling clearer interpretation and analysis of EEG signal complexity. In implementation, this hybrid method generally follows a pipeline: raw EEG preprocessing -> ICA decomposition -> component selection -> PCA transformation -> feature analysis. The entire process facilitates efficient noise reduction, artifact removal, and pattern identification in multichannel EEG data. In conclusion, this integrated approach is particularly well-suited for EEG signal processing and has gained widespread application in current neuroscience research, with implementations available in toolboxes like EEGLAB or MNE-Python using functions such as fastica() and pca().