{'fraction_non_alphanumeric': 0.009445100354191263, 'fraction_numerical': 0.0023612750885478157, 'mean_word_length': 5.625, '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}}
BLIND EXTRACTION OF MICROSLEEP EVENTS USING KALMAN FILTERING

An adaptive biosignal analysis method for the detection and extraction of microsleep events is presented. We proposed a blind source extraction method applying Kalman filtering to extract the microsleep events. This is achieved by an adaptive algorithm in which the cost function jointly estimates the kurtosis and a measure of nonlinearity. Next, Kalman filtering is applied to blindly extract the signal of interest. This method was applied to the electroencephalogram and electroculogram recorded of 23 young volunteers while performing monotonic overnight driving in a real car driving simulation laboratory. The extracted microsleep signals can then be used for driving simulation pilot studies for alertness monitoring, and to trigger the activation of alertness countermeasures.