{'fraction_non_alphanumeric': 0.02321981424148607, 'fraction_numerical': 0.005159958720330237, 'mean_word_length': 5.214743589743589, '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}}
Hand Movement Prediction Based on EEG signals by Combining MEMD and CSP

For prosthetic limb control and rehabilitation training of disabilities, it is important to use electroencephalography (EEG) to recognize different hand movements to assist the disabilities. In this paper, we proposed a novel method by combining multivariate empirical mode decomposition (MEMD) and common spatial pattern (CSP) to extract EEG features, and achieved the prediction of hand movement. Thirty-channel EEG signals and four-channel EMG signals were acquired during the experiment, and the EEG signals were captured one second prior to the beginning of detected hand movement based on the surface electromyography (EMG) signals. MEMD was applied to decomposing the pre-processed EEG signals into several multivariate intrinsic mode functions (IMFs) and CSP was used to extract the features of IMFs. Then, the principal component analysis (PCA) was used to reduce the feature dimension. In the end, six one-versus-one support vector machines were applied to classify the EEG signals. Ten subjects participated in this experiment consisting of four types of hand movements. EEG signals were divided into a training set and a test set by five-fold cross-validation. The average classification accuracy was regarded as the final results. The optimal single IMF and combination IMFs for classification were analyzed in this study. The results showed that the proposed method had a good performance in predicting the upcoming hand movements by classifying the signals prior to the detected hand movement. The combination of IMF1, IMF2, and IMF3 revealed the highest average classification accuracy of 82.67%, and the average kappa coefficient was 0.77, which indicated the predicted results were highly consistent with the actual results. It indicates that the proposed method combining MEMD and CSP is suitable for predicting different types of hand movements.