{'fraction_non_alphanumeric': 0.02631578947368421, 'fraction_numerical': 0.0030959752321981426, 'mean_word_length': 5.221153846153846, '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}}
Sensitive Measures of Condition Change in EEG Data

We present a new, robust, model-independent technique for measuring condition change in nonlinear data. We define indicators of condition change by comparing distribution functions (DF) defined on the attractor for time windowed data sets via L{sub 1}-distance and {chi}{sup 2} statistics. The new measures are applied to EEG data with the objective of detecting the transition between non-seizure and epileptic brain activity in an accurate and timely manner. We find a clear superiority of the new metrics in comparison to traditional nonlinear measures as discriminators of condition change.