{'fraction_non_alphanumeric': 0.017957351290684626, 'fraction_numerical': 0.001122334455667789, 'mean_word_length': 5.809160305343512, '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}}
Nonparametric Discriminant Projections for Improved Myoelectric Classification

Linear discriminant analysis (LDA) is widely used for classification of myoelectric signals and it has been shown to outperform simple classifiers such as k-Nearest Neighbour (kNN). However the normality assumption of the LDA may cause its performance to decrease when the distribution of the feature space is far from Gaussian. In this study we investigate whether nonparametric discriminant (NDA) projections in combination with kNN classifiers can significantly decrease the classification error. Data sets based on both surface and intramuscular electromyography (EMG) were used in order to solve classification problems of up to 9 classes, including simultaneous movements. Results showed that in all data sets, the classification error was significantly lower when using NDA projections compared with LDA.