Focused on the world-wide issue of improving the accuracy of emotion recognition, this paper proposes an electroencephalogram (EEG) signal feature extraction algorithm based on wavelet packet energy entropy and auto-regressive (AR) model. The auto-regressive process can be approached to EEG signal as much as possible, and provide a wealth of spectral information with few parameters. The wavelet packet entropy reflects the spectral energy distribution of the signal in each frequency band. Combination of them gives a better reflect of the energy characteristics of EEG signals. Feature extraction and fusion are implemented based on kernel principal component analysis. Six emotional states from a public multimodal database for emotion analysis using physiological signals (DEAP) are recognized. The results show that the recognition accuracy of the proposed algorithm is more than 90%, and the highest recognition accuracy is 99.33%. It indicates that this algorithm can extract the feature of EEG emotion well, and it is a kind of effective emotion feature extraction algorithm, providing support to emotion recognition.
Emotion recognition refers to the process of determining and identifying an individual's current emotional state by analyzing various signals such as voice, facial expressions, and physiological indicators etc. Using electroencephalogram (EEG) signals and virtual reality (VR) technology for emotion recognition research helps to better understand human emotional changes, enabling applications in areas such as psychological therapy, education, and training to enhance people’s quality of life. However, there is a lack of comprehensive review literature summarizing the combined researches of EEG signals and VR environments for emotion recognition. Therefore, this paper summarizes and synthesizes relevant research from the past five years. Firstly, it introduces the relevant theories of VR and EEG signal emotion recognition. Secondly, it focuses on the analysis of emotion induction, feature extraction, and classification methods in emotion recognition using EEG signals within VR environments. The article concludes by summarizing the research’s application directions and providing an outlook on future development trends, aiming to serve as a reference for researchers in related fields.
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.
Steady-state visual evoked potential (SSVEP) is one of the commonly used control signals in brain-computer interface (BCI) systems. The SSVEP-based BCI has the advantages of high information transmission rate and short training time, which has become an important branch of BCI research field. In this review paper, the main progress on frequency recognition algorithm for SSVEP in past five years are summarized from three aspects, i.e., unsupervised learning algorithms, supervised learning algorithms and deep learning algorithms. Finally, some frontier topics and potential directions are explored.
Human motion recognition (HAR) is the technological base of intelligent medical treatment, sports training, video monitoring and many other fields, and it has been widely concerned by all walks of life. This paper summarized the progress and significance of HAR research, which includes two processes: action capture and action classification based on deep learning. Firstly, the paper introduced in detail three mainstream methods of action capture: video-based, depth camera-based and inertial sensor-based. The commonly used action data sets were also listed. Secondly, the realization of HAR based on deep learning was described in two aspects, including automatic feature extraction and multi-modal feature fusion. The realization of training monitoring and simulative training with HAR in orthopedic rehabilitation training was also introduced. Finally, it discussed precise motion capture and multi-modal feature fusion of HAR, as well as the key points and difficulties of HAR application in orthopedic rehabilitation training. This article summarized the above contents to quickly guide researchers to understand the current status of HAR research and its application in orthopedic rehabilitation training.
The number of people with physical disabilities is increasing year by year, and the trend of population aging is more and more serious. In order to improve the quality of the life, a control system of accessible home environment for the patients with serious disabilities was developed to control the home electrical devices with the voice of the patients. The control system includes a central control platform, a speech recognition module, a terminal operation module, etc. The system combines the speech recognition control technology and wireless information transmission technology with the embedded mobile computing technology, and interconnects the lamp, electronic locks, alarms, TV and other electrical devices in the home environment as a whole system through a wireless network node. The experimental results showed that speech recognition success rate was more than 84% in the home environment.
ObjectiveTo investigate the preoperative cognition of the patients undergoing daytime ophthalmic fundus surgery and understand their needs of health education, so as to provide an evidence for efficient and accurate preoperative health education services within the limited time of the ophthalmic day fundus surgery.MethodsThe convenient sampling method was used to select the patients who met the inclusion criteria in the ambulatory operating room of Beijing Tongren Hospital, Capital Medical University from December 2017 to May 2018. The study included three parts: the general information of the patients, the preoperative cognition of the patients, and the needs for health education service of the patients. Questionnaires were designed according to the research purpose and method, which were distributed and recovered by professionals.ResultsA total of 112 patients were included. Among them, the cognitive scores of operation process (2.57±0.56), preoperative diet (2.58±0.59), preoperative medication (2.60±0.64), and psychological status (2.58±0.65) were relatively low. More health education services were needed in three aspects: the cognition of operation details [operation duration (85.71%), surgeons (79.46%), operation start time (76.79%)], intraoperative cooperation (90.18%), and intervention for preoperative anxiety (78.57%).ConclusionNurses should formulate the contents of preoperative health education according to the preoperative cognition and nursing needs of patients, so as to provide efficient and accurate health education services for patients.
OBJECTIVE: To investigate the role of direct and indirect recognition in pig-to-man xenotransplantation. METHODS: Taken the peripheral blood lymphocytes (PBLC) from three Neijiang pigs and two humans as stimulators and respondors, the one-way mixed lymphatic reactions (MLR) of xenograft were carried out, and allo- and self-PBLC as control. RESULTS: Among the three patterns of MLR, syngeneic was MLR the lowest in proliferation, the allogenic MLR was the highest, and the xenogenic MLR was medium. The PBLCs from humans and pigs were matched on HLA-A, B, DR and DQ by means of modified Terasaki assay. The match on pigs was failure because of the pre-existing natural xenogenic antibody in the testing serum. CONCLUSION: The results suggest that the degree of MHC matching still affect the rejection in xenotransplantation, but the present serum assay of MHC matching is not fit for pig.
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.
Objective To study the distribution of HIV/AIDS high-risk population, HIV infection and the main risk factors for developing HIV/AIDS’ controllable measures and exploring appropriate health education and behavior intervention models. Methods A total of 360 commercial sex workers (CSW) joined together through convenience sampling and 360 drug users (DU) joined together through convenience sampling or snow-balling sampling whose relevant behavior factors were investigated by questionnaires. Results The general rate of knowing knowledge about AIDS was 75.2% among 360 CSW, 67.8% CSW used condom in commercial sex activities; none of 149 CSW blood samples was detected HIV or syphilis antibody positive. The general rate of knowing knowledge about AIDS was 83.7% among 360 DU who injected drugs last month, the rate of sharing needles was 47.6% and the low rate of condom used; 1 HIV antibody and 5 syphilis antibodies positive were found among 198 DU blood samples, so HIV and syphilis infection rate were 0.51%and 2.53%, respectively. Conclusion The rate of HIV infection is a very low level and there are many risk factors among CSW and DU. A good job should be done to integrate AIDS health education with behavioral intervention and the monitoring system for the AIDS/HIV high-risk population should be improved.
