{'fraction_non_alphanumeric': 0.017598343685300208, 'fraction_numerical': 0.0, 'mean_word_length': 5.623287671232877, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Research on EEG signal preprocessing based on ICA fusion EMD

The EEG signal is a random signal with a very complicated mechanism and very faint, and it is very susceptible to interference from electrooculogram artifact signals. This paper proposed an ICA-EMD EEG signal processing algorithm based on the fusion of independent component analysis (ICA) and empirical mode decomposition (EMD), which can remove noisy artifacts from EEG signals with high accuracy. This method firstly decomposes the multichannel EEG signal into statistically independent components by independent component analysis (ICA), and then using EMD filtering achieve the elimination of physiological artifacts of single-component EEG. the experiments show that the ICA-EMD algorithm can effectively retain the neural information in the artifacts and the brain electrical activity signals outside the artifact frequency band, therefore it avoids the loss of important brain electrical signals.