Research Of Key Issues For Wearable Electroencephalogram Sensor And Appliction

With the development of microelectronics,information technology and new materials,sensors,human physiological signals can be collected with high precision,highly sensitive sensors,facilitates the physiological and pathological condition of the human analysis and research,which are widely used drug physiological signal monitoring personnel,astronauts,athletes,mental illness,cardiovascular disease,and therefore based on wearable sensor signal acquisition technology,information processing theory has become a research hotspot.The human brain as the body’s nerve center,dominates human ideology,movement,cognitive functions such as language,the study of brain information has become the focus of Brain and Cognitive Sciences,as wearable EEG sensors became Cognitive Brain one of the important means of scientific research.In this paper,wearable EEG sensor research as the core collection for EEG processing several key issues,study the corresponding denoising and feature extraction algorithms,and 973 projects "based on biological,psychological multimodal information Research on the early warning of potential risks of depression biosensor key technologies " and " psychological health problems of heroin addiction crowd " as the basis,to verify the proposed algorithm in depression,addiction crowd perception of brain information system effectiveness.Since EEG neuronal discharge and transfer an important expression of information,collected through the scalp EEG is very weak,generally between 5uv100 uv,are susceptible to outside interference,so the scientific research institutes and hospitals are shielded by professionals gathering room.The portable wearable EEG sensors are pervasive in the environment,and can wear their own collection,so by the noise shielding compared to the larger room.Thus removing the noise as the primary problem wearable EEG sensors require further study.Aiming at this particularity EEG,wearable EEG sensors developed software and hardware,based on this algorithm to do further research and application verification,innovative research results of this paper are the following four aspects:(1)The raw EEG signal is subject to various noise,such as brain and cognitive function unrelated EOG artifact.In order to remove OAs from the EEG,we have been in a previous study proposed three artifact removal methods,such as independent component analysis(ICA),the wavelet transform and adaptive filtering method.This paper proposes a new hybrid denoising(DWT-Kalman),Discrete Wavelet Transform(DWT)Kalman filter remove EOG artifact in EEG,the accuracy of this model in terms of de-noising with significantly increased.The results show that this method allows error correction original EEG and EEG between smaller MSE was 0.0017;MAE is 0.0052.This method does not rely on any particular electrode,nor on the number of electrodes in,thus more convenient to use.(2)Hardware and software development of the hardware and software of wireless,portable,low power consumption EEG sensor based on Bluetooth module.From the CPU selection,power management and Bluetooth 4.0 low power mode;select the right leg drive(RLD),low-noise power supply isolation,analog preamp,active electrode,isolation and electromagnetic shielding,effectively reducing the external noise;choose Bluetooth 4.0 and timestamp(0.1ms level),to solve the power problem with the data integrity of the wireless communication frame;completion of the internal hardware is automatically turned off detection.PC application software to achieve the main graphical user interface,through which the software can monitor brain wave patterns of EEG data collected through the background check and processing algorithms,and format conversion,the data is stored and with server data synchronization.(3)The relationship between mental state and brain signal in depressed people.This paper relies on 973 research projects,through self-rating scale and MINI scale in Beijing Anding Hospital and the Second Hospital of Lanzhou University,selected patients with depression and healthy as all 47 of the study subjects,through the 3-lead EEG acquisition instrument recording two groups,with the aim relevance feature amount mental state of patients with depression and EEG between thereby enabling to predict the risk of depression.so how can effectively distinguish between two types of EEG characteristics of the population,has become a key issue.During the research,we use the linear theory of modern spectral estimation,extracted EEG absolute power spectra of two populations resting and audio stimulation,the gravity frequency,maximum power and power spectral entropy four features,and extracted with SPSS the statistical analysis features.Statistical analysis showed that,in the resting state,the depression group alpha wave of absolute power and the maximum power is higher than normal;in audio stimulation,absolute power between depression group alpha waves,gravity frequency and maximum power with the normal group significant differences.Person using correlation analysis,compared with the scale resting EEG correlations obtained health questionnaire and the EEG power spectral entropy maximum power and have good correlation.The use of non-linear analysis method,calculate the Renyi entropy of the two populations,C0 complexity,three related dimensions effective features.From the perspective of cognitive neuroscience,cognitive attentional bias is one of the characteristics of depression.That depressed patients to focus more on their own internal mental activity,not sensitive to external stimuli.This is a significant decrease in the activity of the prefrontal region,and with the top-down cognitive control functions related to depression is low.This just confirms our mutual EEG,so portable 3-lead EEG sensors can be applied to predict the risk of depression.(4)The relationship between cognitive neural mechanism and EEG characteristics in heroin addicts.Because the reward is one exception processing hallmark of addictive behavior,reward drug addicts during the withdrawal process and resting EEG through different studies provide a basis for objective assessment and diagnosis of addiction.In this study,we used Three Paradigms,such as the Gamble Task,MIDT(Monetary Incentive Delay Task),attentional capture,and addiction neurocognitive mechanisms by studying populations of heroin,by static interest-state EEG power spectral analysis of addiction and normal people EEG power spectrum compared,delta rhythm and increase beta rhythm,alpha rhythm decreased,indicating that people in heroin addiction withdrawal period emotionally unstable and show anxiety symptoms.The significance of this study lies in the development of wearable EEG sensor,improve the effectiveness of noise removal and feature extraction algorithm,and applied and tested to a depression and heroin addiction two groups.It provides a solid foundation for the 973 project Early Warning of Depression and The Objective Diagnosis of Addiction.It has a certain application reference value for the future development of the system.