Research Of Ubiquitous Electroencephalogram Data Sensing And Awareness

With the development of novel sensor materials and low-power microprocessor research, biosensors have been widely applied to physiological information monitoring on soldiers, astronauts, athletes and patients. Thus, bioinformatics perception theory and technology based on wearable biosensors have shown increasing importance and gained considerable development. Since brain is the most important control center of body, research of EEG(Electroencephalogram) analysis becomes the focus of bioinformatics perception study. Most of mental illnesses and cognitive abilities are closely associated with brain, so the research of EEG based on wearable sensor has become a hot spot. This paper focused on methodology of EEG data sensing and awareness, proposed model and algorithm to solve several key questions in EEG information processing. Based on the Seventh Framework Programmem of EU "Online Predictive Tools for Intervention in Mental Illness," the model and algorithm are proved to be efficient in bioinformatics sensing systems.The EEG signal is so week that many institutes and hospitals set special screened room where professional persons operate the machines. But in the ubiquitous environment filled with noises, users have to do it themselves. It’s important to research the key problems of EEG data sensing and awareness related to ubiquitous environment. This paper solved key questions in the new methodology and proposed 3 innovative parts:(1) A novel assessment model of EEG signal quality:When the user operates EEG sensor out of the hospital environment and the sensor electrode is not properly attached to the forehead, the EEG signal collected then will be not available, resulting in failed experiment. Therefore, it’s needed to design a quick and effective way to assess the quality of the EEG signal so as to guide the user to determine EEG data availability. But the algorithm to assess the quality of the EEG is very scarce right now, and most reaearches concentrated in the skin-electrode impedance. In this paper, a novel assessment model to assess the raw EEG signal quality is proposed which is based on the idea of fuzzy comprehensive evaluation, large amounts of data, and experts’opinion evaluation. This model can work in EEG sensor fastly and thus users can do self-test. Thus the model is proved to be efficient and solves problem of EEG signal quality control in begin of project.(2) EOG noise removal algorithm under less leads EEG:Electrodes of wearable EEG sensor are placed on the forehead, where is close to eyes. Thus the EEG is susceptible to EOG(Electrooculogram). This paper studied two EOG noise removal algorithms with less leads of EEG. One is a combination of independent component analysis and wavelet transform. Wavelet transform is used to constructed EOG reference signal, with which the independent component analysis methord works to extract the real EOG noise. Another algorithm is a combination of Adaptive Noise Cancellation and Discrete Wavelet Transformation, the reference of EOG is reconstructed by Discrete Wavelet Transformation and used as input of Adaptive Noise Cancellation to remove real EOG noise. Both of them are suitable for small portable EEG sensor works under less leads to remove the EOG and keep real EEG, and have been widely used in data processing in this project and proved to be efficient.(3) Analysis of EEG features related to mental stress:EEG features are related to specific application. The purpose of this project is to take advantage of perceived biological information to assess whether someone suffers mental stress, so the exploration of stress-related EEG features is critical. Based on the experimental data (Scale and EEG data) from China, Spain and Switzerland, this paper classifid the characteristics of EEG with KNN and naive Bayes classification algorithm to find the correlation between mental stress and EEG. Besides traditional linear features, some new non-linear features are worked out related to mental stress.The aim of this paper is find out and intervene mental stress in the beginning. In this highly competitive and fast-tempo society, incidence of psychological and mental illnesses continues to rise up, leading to tremendous healthcare burden and economic losses. At the same time, various deficiencies such as the lack of health care professionals, poor timeliness of limited medical treatment methods, are very popular in many countries. The project of "Online Predictive Tools for Intervention in Mental Illness" tries to collect the body’s biological information with wearable biosensor system, get their electronic questionnaires and scales, and with help of computerized cognitive behavioral therapy, to research the risk of psychological or psychiatric illness, and provide early warning and online intervention. Based on the novel wearable EEG sensor combined with the results above, this paper studied the key problems of ubiquitous EEG data sensing and awareness, and put it to project of "Online Predictive Tools for Intervention in Mental Illness" to achieve a stress clinic system to contribute certain social significance.