{'fraction_non_alphanumeric': 0.022375215146299483, 'fraction_numerical': 0.0, 'mean_word_length': 5.466666666666667, '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}}
A comparison between wavelet families to compress an EEG signal

Wavelet Transform (WT) is a widely technique used to compress a biomedical signal. This algorithm has different orthonormal basis functions coined as families. In this work EEG signals are compressed. Also, different Wavelet families are presented in order to compare the performances of each algorithm under two different criteria: quantitative and qualitative. Additionally, in order to compare our results, a combined algorithm (Block Sparse Bayesian Learning and Compressed Sensing) is taken from the literature.