Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.
Bayes Theorem ; Brain ; physiology ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Magnetoencephalography ; Multivariate Analysis ; Principal Component Analysis

To solve the complex interaction problems of hepatitis disease classification, we proposed a lasso method (least absolute shrinkage and selection operator method) with feature interaction. First, lasso penalized function and hierarchical convex constraint were added to the interactive model which is newly defined. Then the model was solved with the convex optimal method combining Karush-Kuhn-Tucker (KKT) condition with generalized gradient descent. Finally, the sparse solution of the main effect features and interactive features were derived, and the classification model was implemented. The experiments were performed on two liver data sets and proved that features interaction contributed to the classification of liver diseases. The experimental results showed that the feature interaction lasso method was of strong explanatory ability, and its effectiveness and efficiency were superior to those of lasso, of all pair-wise lasso, support vector machine (SVM) method, K nearest neighbor (KNN) method, linear discriminant analysis (LDA) classification method, etc.
Algorithms ; Cluster Analysis ; Discriminant Analysis ; Humans ; Liver Diseases ; classification ; Support Vector Machine

Six kinds of erythemato-squamous diseases have been common skin diseases, but the diagnosis of them has always been a problem. The quantitative data processing method is not suitable for erythemato-squamous data because they are categorical qualitative data. This paper proposed a new method based on group lasso penalized classification for the feature selection and classification for erythemato-squamous data with categorical qualitative data. The first categorical data of 33 dimensions were changed by the virtual code, and then 34th dimension age data were discretized and changed by the virtual code. Then the encoded data were grouped according to class group and variable group. Lastly Group Lasso penalized classification was executed. The classified accuracy of 10-fold cross validation was 98.88% ± 0.002 3%. Compared with those of other method in the literature, this new method is simpler, and better for effect and efficiency, and has stronger interpretability and stronger stability.
Algorithms ; Computational Biology ; methods ; Humans ; Reproducibility of Results ; Skin Diseases ; classification ; diagnosis

Erythemato-squamous diseases are a general designation of six common skin diseases, of which the differential diagnosis is a difficult problem in dermatology. This paper presents a new method based on virtual coding for qualitative variables and multinomial logistic regression penalized via elastic net. Considering the attributes of variables, a virtual coding is applied and contributes to avoid the irrationality of calculating nominal values directly. Multinomial logistic regression model penalized via elastic net is thence used to fit the correlation between the features and classification of diseases. At last, parameter estimations can be attained through coordinate descent. This method reached accuracy rate of 98.34% +/- 0.0027% using 10-fold cross validation in the experiments. Our method attained equivalent accuracy rate compared to the results of other methods, but steps are simpler and stability is higher.
Diagnosis, Differential ; Humans ; Logistic Models ; Skin Diseases ; diagnosis

The brain computer interface (BCI) can be used to control external devices directly through electroencephalogram (EEG) information. A multi-linear principal component analysis (MPCA) framework was used for the limitations of tensor form of multichannel EEG signals processing based on traditional principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA). Based on MPCA, we used the projection of tensor-matrix to achieve the goal of dimensionality reduction and features exaction. Then we used the Fisher linear classifier to classify the features. Furthermore, we used this novel method on the BCI competition II dataset 4 and BCI competition N dataset 3 in the experiment. The second-order tensor representation of time-space EEG data and the third-order tensor representation of time-space-frequency BEG data were used. The best results that were superior to those from other dimensionality reduction methods were obtained by much debugging on parameter P and testQ. For two-order tensor, the highest accuracy rates could be achieved as 81.0% and 40.1%, and for three-order tensor, the highest accuracy rates were 76.0% and 43.5%, respectively.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Principal Component Analysis

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.
Algorithms ; Brain ; physiology ; Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Humans ; Magnetoencephalography ; Principal Component Analysis

Using electroencephalogram (EEG) signal to control external devices has always been the research focus in the field of brain-computer interface (BCI). This is especially significant for those disabilities who have lost capacity of movements. In this paper, the P300-based BCI and the microcontroller-based wireless radio frequency (RF) technology are utilized to design a smart home control system, which can be used to control household appliances, lighting system, and security devices directly. Experiment results showed that the system was simple, reliable and easy to be populirised.
Brain ; physiology ; Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Humans

Electroencephalogram (EEG) classification for brain-computer interface (BCI) is a new way of realizing human-computer interreaction. In this paper the application of semi-supervised sparse representation classifier algorithms based on help training to EEG classification for BCI is reported. Firstly, the correlation information of the unlabeled data is obtained by sparse representation classifier and some data with high correlation selected. Secondly, the boundary information of the selected data is produced by discriminative classifier, which is the Fisher linear classifier. The final unlabeled data with high confidence are selected by a criterion containing the information of distance and direction. We applied this novel method to the three benchmark datasets, which were BCI I, BCI II_IV and USPS. The classification rate were 97%, 82% and 84.7%, respectively. Moreover the fastest arithmetic rate was just about 0. 2 s. The classification rate and efficiency results of the novel method are both better than those of S3VM and SVM, proving that the proposed method is effective.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; classification ; Humans

Danshen (Salvia miltiorrhiza Bunge) hairy roots were obtained by infecting Danshen leaves with Agrobacterium rhizogenes 9402. Besides rosmarinic acid (RA) and salvianolic acid B (SAB), the hairy root could also produce salvianolic acid K (SAK), salvianolic acid L, ethyl salvianolic acid B (ESAB), methyl salvianolic acid B (MSAB), and a compound with a molecular weight of 538 (compound 538) identified by using LC-MS. Effects of methyl jasmonate (MeJA) and yeast elicitor (YE) on the accumulation of these compounds had been investigated. MeJA increased the accumulation of SAB, RA, SAK, and compound 538 from 4.21%, 2.48%, 0.29%, and 0.01% of dry weight to 7.11%, 3.38%, 0.68%, and 0.04%, respectively. YE stimulated the biosynthesis of RA from 2.83% to 5.71%, but depressed the synthesis of SAB, SAK and compound 538. It was indicated in all the results that these Danshen hairy roots could be used as alternative resources to produce salvianolic acids. Analysis of the content variation of these compounds after elicitation suggested that SAK and compound 538 might be the intermediates in the biosynthesis from RA to SAB in Danshen hairy roots.

Discriminative common vector (DCV) is an effective method that was proposed for the small sample size problems of face recognition. There is the same problem in brain-computer interface (BCI). Using directly the linear discriminative analysis (LDA) could result in errors because of the singularity of the within-class matrix of data. In our studies, we used the DCV method from the common vector theory in the within-class scatter matrix of data of all classes, and then applied eigenvalue decomposition to the common vectors to obtain the final projected vectors. Then we used kernel discriminative common vector (KDCV) with different kernel. Three data sets that include BCI Competition I data set, Competition II data set IV, and a data set collected by ourselves were used in the experiments. The experiment results of 93%, 77% and 97% showed that this feature extraction method could be used well in the classification of imagine data in BCI.
Algorithms ; Artificial Intelligence ; Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Face ; anatomy & histology ; Humans ; Pattern Recognition, Automated ; methods ; Principal Component Analysis ; Sample Size ; Signal Processing, Computer-Assisted ; User-Computer Interface