{'fraction_non_alphanumeric': 0.02081447963800905, 'fraction_numerical': 0.0, 'mean_word_length': 5.583333333333333, '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}}
Two-dimensional SVD for event detection in dynamic functional brain networks

In recent years, there has been a growing interest in analyzing functional connectivity networks estimated from neuroimaging technologies using graph theory. Previous studies of the functional brain networks have focused on extracting static or time-independent networks to describe the long-term behavior of brain activity. In this paper, we propose a dynamic functional brain network tracking and summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed approach is based on two-dimensional SVD of the three-mode tensor representation of dynamic graphs. First, the event intervals are identified based on the change in the reconstruction error in the lower dimensional space and then the activity in the event intervals are summarized. The proposed method is evaluated for characterizing time-varying network dynamics from event-related potential (ERP) data indexing the well-known error-related negativity (ERN) component related to cognitive control.