{'fraction_non_alphanumeric': 0.018151815181518153, 'fraction_numerical': 0.0, 'mean_word_length': 5.776536312849162, '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}}
Comparison of complexity measures using two complex system analysis methods applied to the epileptic ECoG

A complex system analysis has been widely applied to examine the characteristics of an electroencephalogram (EEG) in health and disease, as well as the dynamics of the brain. In this study, two complexity measures, the correlation dimension and the spectral exponent, are applied to electrocorticogram (ECoG) data from subjects with epilepsy obtained during different states (seizure and non-seizure) and from different brain regions, and the complexities of ECoG data obtained during different states and from different brain regions are examined. From the computational results, the spectral exponent obtained from the wavelet-based fractal analysis is observed to provide information complementary to the correlation dimension derived from the nonlinear dynamical-systems analysis. ECoG data obtained during seizure activity have smoother temporal patterns and are less complex than data obtained during non-seizure activity. In addition, significant differences between these two ECoG complexity measures exist when applied to ECoG data obtained from different brain regions of subjects with epilepsy.