MATLAB Implementation of FastICA Algorithm for EEG Signal Denoising

In the field of EEG signal processing, the FastICA algorithm serves as a highly effective method for removing ocular artifacts and other noise sources to achieve proper EEG signal denoising. This algorithm operates on the principle of independent component analysis (ICA), which separates mixed signals into statistically independent components, thereby eliminating interference signals and extracting meaningful EEG features. The MATLAB implementation typically involves several key steps: First, the raw EEG data is preprocessed through centering (subtracting the mean) and whitening (decorrelation and variance normalization) using functions like 'zscore' or custom whitening routines. The core algorithm then employs fixed-point iteration to maximize non-Gaussianity through nonlinear functions (such as 'tanh' or 'cube'), which helps identify independent components corresponding to neural activity versus artifacts. To further enhance EEG signal quality, additional signal processing techniques can be integrated into the workflow. These may include: - Digital filtering (using 'filter' or 'filtfilt' functions) for frequency-based noise removal - Artifact estimation methods like regression-based correction - Electrode coupling correction algorithms to address cross-talk between channels The separated components are then carefully examined through visual inspection or automated detection to identify and remove artifact-related components before reconstructing the clean EEG signal. Practical implementation considerations include proper parameter selection for convergence tolerance, maximum iterations, and nonlinearity functions, which can significantly impact separation performance and computational efficiency.