{'fraction_non_alphanumeric': 0.03854389721627409, 'fraction_numerical': 0.026409707351891507, 'mean_word_length': 5.52093023255814, '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}}
Sample Entropy enhanced wavelet-ICA denoising technique for eye blink artifact removal from scalp EEG dataset

Scalp Electroencephalogram (EEG) recordings are usually contaminated with a variety of artifacts, which can be removed by threshold-based classifiers, Principal Component Analysis, Independent Component Analysis (ICA), wavelet-based multi-resolution analysis, or higher order statistics. In this paper we propose Sample Entropy, a self-sufficient statistical measure to identify the eye blink related independent components and Haar wavelet decomposition to subsequently denoise these components. The proposed method identified the blink artifactual components with an accuracy of 88% in our pilot study (N=4). The results demonstrated the improved performance of eye-blink artifacts removal with the neural activity intact in terms of Mutual Information (1.27 / 0.318 / 1.15), Correlation coefficient (0.574 / 0.369/ 0.569), and Standard deviation ratio (0.559/ 0.375 / 0.551) in comparison to standard Zeroing-ICA and wavelet-ICA based techniques, respectively. Instead of human expertise intervention to identify the eye blink components after Extended Infomax ICA decomposition, the algorithm offers potential for automation. This algorithm also offers advantage of being computationally fast and inexpensive, and does not require additional Electrooculographic signals for referencing.