The research of sleep staging is not only a basis of diagnosing sleep related diseases but also the precondition of evaluating sleep quality, and has important clinical significance. In recent years, the research of automatic sleep staging based on computer has become a hot spot and got some achievements. The basic knowledge of sleep staging and electroencephalogram (EEG) is introduced in this paper. Then, feature extraction and pattern recognition, two key technologies for automatic sleep staging, are discussed in detail. Wavelet transform and Hilbert-Huang transform, two methods for feature extraction, are compared. Artificial neural network and support vector machine (SVM), two methods for pattern recognition are discussed. In the end, the research status of this field is summarized, and development trends of next phase are pointed out.
Based on bioelectrical impedance theory and pattern recognition algorithm, we in this study measured varieties of people's bioelectrical impedance in hands and identified different people according to their bioelectrical impedance. We designed a bioelectrical impedance collection circuit with AD5933 chip to measure the impedance in different people's hands, and we obtained the bioelectrical impedance spectrum for each person under 1-100 kHz electrical stimulation. We calculated the segmentation slopes of bioelectrical impedance spectrum, and took the slopes as characteristic parameters. In order to promote the recognition rate and prevent the overfitting of the model, we divided the people into the training set and the test set, and designed a 3 layer back propagation neural network model to train and test the samples. The results showed that back propagation neural network model could identify the test set effectively. The recognition rate of the training sets was as high as 97.62%, recognition rate of validation sets was 88.79%, recognition rate of test sets was 86.34%, and the synthetical recognition rate was 94.22%. It gives a clue that the network can perfectly recognize people in the training network as well as strangers that comes from the outside of the tests. Our work can verify the feasibility and reliability of using bioelectrical impedance and pattern recognition algorithm for identification, and can provide a simple and supplementary way to identify people.
Alzheimer’ s disease is the most common kind of dementia without effective treatment. Via early diagnosis, early intervention after diagnosis is the most effective way to handle this disease. However, the early diagnosis method remains to be studied. Neuroimaging data can provide a convenient measurement for the brain function and structure. Brain structure network is a good reflection of the fiber structural connectivity patterns between different brain cortical regions, which is the basis of brain’s normal psychology function. In the paper, a brain structure network based on pattern recognition analysis was provided to realize an automatic diagnosis research of Alzheimer’s disease and gray matter based on structure information. With the feature selection in pattern recognition, this method can provide the abnormal regions of brain structural network. The research in this paper analyzed the patterns of abnormal structural network in Alzheimer’s disease from the aspects of connectivity and node, which was expected to provide updated information for the research about the pathological mechanism of Alzheimer’s disease.
Brain-computer interface (BCI) provides a direct communicating and controlling approach between the brain and surrounding environment, which attracts a wide range of interest in the fields of brain science and artificial intelligence. It is a core to decode the electroencephalogram (EEG) feature in the BCI system. The decoding efficiency highly depends on the feature extraction and feature classification algorithms. In this paper, we first introduce the commonly-used EEG features in the BCI system. Then we introduce the basic classical algorithms and their advanced versions used in the BCI system. Finally, we present some new BCI algorithms proposed in recent years. We hope this paper can spark fresh thinking for the research and development of high-performance BCI system.
Studying the ability of the brain to recognize different odors is of great significance in the assessment and diagnosis of olfactory dysfunction. The wavelet energy moment (WEM) was proposed as a feature of olfactory electroencephalogram (EEG) signal and used for odor classification. Firstly, the olfactory evoked EEG data of 13 odors were collected by an experiment. Secondly, the WEM was extracted from olfactory evoked EEG data as the signal feature, and the power spectrum density (PSD), approximate entropy, sample entropy and wavelet entropy were used as the contrast features. Finally, k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF) and decision tree classifier were used to identify different odors. The results showed that using the above four classifiers, the classification accuracy of WEM feature was higher than other features, and the k-NN classifier combined with WEM feature had the highest classification accuracy (91.07%). This paper further explored the characteristics of different EEG frequency bands, and found that most of the classification accuracy based on the features of γ band was better than that of the full band and other bands, among which the WEM feature of the γ band combined with the k-NN classifier had the highest classification accuracy (93.89 %). The research results of this paper could provide a new objective basis for the evaluation of olfactory function. On the other hand, it could also provide new ideas for the study of olfactory-induced emotions.
Vision is an important way for human beings to interact with the outside world and obtain information. In order to research human visual behavior under different conditions, this paper uses a Gaussian mixture-hidden Markov model (GMM-HMM) to model the scanpath, and proposes a new model optimization method, time-shifting segmentation (TSS). The TSS method can highlight the characteristics of the time dimension in the scanpath, improve the pattern recognition results, and enhance the stability of the model. In this paper, a linear discriminant analysis (LDA) method is used for multi-dimensional feature pattern recognition to evaluates the rationality and the accuracy of the proposed model. Four sets of comparative trials were carried out for the model evaluation. The first group applied the GMM-HMM to model the scanpath, and the average accuracy of the classification could reach 0.507, which is greater than the opportunity probability of three classification (0.333). The second set of trial applied TSS method, and the mean accuracy of classification was raised to 0.610. The third group combined GMM-HMM with TSS method, and the mean accuracy of classification reached 0.602, which was more stable than the second model. Finally, comparing the model analysis results with the saccade amplitude (SA) characteristics analysis results, the modeling analysis method is much better than the basic information analysis method. Via analyzing the characteristics of three types of tasks, the results show that the free viewing task have higher specificity value and a higher sensitivity to the cued object search task. In summary, the application of GMM-HMM model has a good performance in scanpath pattern recognition, and the introduction of TSS method can enhance the difference of scanpath characteristics. Especially for the recognition of the scanpath of search-type tasks, the model has better advantages. And it also provides a new solution for a single state eye movement sequence.
Error self-detection based on error-related potentials (ErrP) is promising to improve the practicability of brain-computer interface systems. But the single trial recognition of ErrP is still a challenge that hinters the development of this technology. To assess the performance of different algorithms on decoding ErrP, this paper test four kinds of linear discriminant analysis algorithms, two kinds of support vector machines, logistic regression, and discriminative canonical pattern matching (DCPM) on two open accessed datasets. All algorithms were evaluated by their classification accuracies and their generalization ability on different sizes of training sets. The study results show that DCPM has the best performance. This study shows a comprehensive comparison of different algorithms on ErrP classification, which could give guidance for the selection of ErrP algorithm.
Objective To review targeted muscle reinnervation (TMR) surgery for the construction of intelligent prosthetic human-machine interface, thus providing a new clinical intervention paradigm for the functional reconstruction of residual limbs in amputees. MethodsExtensively consulted relevant literature domestically and abroad and systematically expounded the surgical requirements of intelligent prosthetics, TMR operation plan, target population, prognosis, as well as the development and future of TMR. Results TMR facilitates intuitive control of intelligent prostheses in amputees by reconstructing the “brain-spinal cord-peripheral nerve-skeletal muscle” neurotransmission pathway and increasing the surface electromyographic signals required for pattern recognition. TMR surgery for different purposes is suitable for different target populations. Conclusion TMR surgery has been certified abroad as a transformative technology for improving prosthetic manipulation, and is expected to become a new clinical paradigm for 2 million amputees in China.