EEG Lead Signal Processing with MATLAB: Denoising, Artifact Removal, and Topographic Mapping

In this experiment, we acquired EEG lead signals and performed denoising and artifact removal using MATLAB. The denoising implementation involved multiple algorithmic approaches: FastICA function from the FastICA_25 folder for blind source separation, PCA for dimensionality reduction, and artifact subtraction (av_sub) for direct noise removal. We executed hybrid denoising procedures through ica_step1 and ica_step2&pca modules that combine independent component analysis with principal component analysis. Wavelet entropy computation (wave_entro) was employed for signal complexity analysis. For visualization, we utilized topoplot as the primary topographic mapping function and topo as the main program for generating brain topographic maps, with eloc32 and eloc16 providing electrode position configurations for 32-channel and 16-channel setups respectively. Data formatting was handled by get_txt which converts raw EEG data into text format, while readdata enables sequential processing of multiple text files from designated folders. These comprehensive processing steps resulted in accurate and clear brain topographic representations suitable for clinical and research applications.