{'fraction_non_alphanumeric': 0.023874488403819918, 'fraction_numerical': 0.0034106412005457027, 'mean_word_length': 5.242553191489361, '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 on Feature Extraction and Classification of EEG Signals Based on Multitask Motor Imagination

In order to improve the accuracy of EEG signals extraction and classification, a new feature extraction and classification method of EEG signals based on multi-task motor imagination is proposed. First, the empirical mode decomposition method is used to decompose the EEG signal into several intrinsic mode functions, and then the fast independent component analysis algorithm is used for the obtained intrinsic mode functions, so as to finally obtain the denoised EEG signal. Aiming at the four types of task modes of motor imagery in brain-computer interaction, this paper adopts the feature extraction algorithm of wavelet packet decomposition (WPD) fusion common spatial pattern (CSP). First, the input EEG signal is wavelet packet decomposition, and then the CSP feature extraction method is used to perform feature extraction for the four types of EEG signals under the “one-to-many” strategy. In terms of classification, a four-task classification method based on support vector machine (SVM) is designed. The experimental results show that the classification accuracy of the four types of motor imagery EEG signals using this paper method is 78.3%, compared with the pure CSP feature extraction, the accuracy rate is increased by 7.8%. It proves that the denoising method, feature extraction and classification method proposed in this paper are effective.