{'fraction_non_alphanumeric': 0.02368692070030896, 'fraction_numerical': 0.005149330587023687, 'mean_word_length': 5.267741935483871, '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}}
EEG classification in MI-BCI applications based on double constraint nonnegative matrix factorization.

OBJECTIVE
Brain computer interface (BCI) aims at providing a new way of communication between the human brain and external devices. One of the major tasks associated with the BCI system is to improve classification performance of motor imagery (MI) signal. Electroencephalogram (EEG) signals are widely used for the MI BCI system. The raw EEG signals are usually non-stationary time series with weak class property, degrading the classification performance.


APPROACH
Nonnegative matrix factorization (NMF) has been successfully applied to pattern extraction which provides meaningful data presentation. However, NMF is unsupervised and cannot make use of the label information. Based on the label information of MI EEG data, we propose a novel method, called double constrained nonnegative matrix factorization (DCNMF), to improve the classification performance of NMF on MI BCI. The proposed method constructs a couple of label matrices as the constraints on the NMF procedure to make the EEGs with the same class labels have the similar representation in the low dimensional space, while the EEGs with different class labels have dissimilar representation as much as possible. Accordingly, the extracted features have obtain obvious class property, which is optimal to the classification of MI EEG.


MAIN RESULTS
This study is conducted on the BCI competition III datasets (I and IVa). The proposed method has helped achieve high averaged accuracy across two datasets (79.00% for dataset I, 77.78% for dataset IVa), it performed better than the existing studies in the literature by about10%.


SIGNIFICANCE
Our study provides a novel solution for the MI BCI analysis from the perspective of label constraint, it provides convenience for semi-supervised learning of features and significantly improves the classification performance.