{'fraction_non_alphanumeric': 0.024299065420560748, 'fraction_numerical': 0.0065420560747663555, 'mean_word_length': 5.155172413793103, '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}}
[Research of classification about BCI based on the signals energy].

Aiming at the issue of motor imagery electroencephalography (EEG) pattern recognition in the research of brain-computer interface (BCI), a power feature method based on discrete wavelet packet decomposition is proposed for the channels C3 and C4. Firstly, a six-border Butterworth filter is used to denoise the two-channel EEG signals. Secondly, two-channel EEG signals are decomposed to five levels using Daubechies wavelet and the fourth level and the fifth level are chosen to reconstruct the signals and compute its power feature. Finally, linear discriminant analysis (LDA) is utilized to classify the feature and the Kappa value is utilized to measure the accuracy of the classifier. This method is applied to the standard dataset BCICIV_2b-gdf of BCI Competition 2008, and experimental results show that this method reflect the feature of event-related synchronization and event-related desynchronization obviously and it is an effective way to classify the EEG patterns in the research of BCI.