ObjectiveTo design multi-adaptive filter based on radial basis function (MAF-RBF) for efficiently extracting somatosensory evoked potential (SEP) in real-time SEP monitoring.MethodsWith the optimization of important parameters that influence the performance of radial basis function neural network,the performance of extracting SEP was compared to that of a multi-adaptive filter (MAF),which developed from the combination of well-developed adaptive noise canceller and adaptive signal enhancer.ResultsIn this simulation study,the outputs of MAF-RBF showed a similar waveform with SEP template signals,and a smoother waveform than the.output of MAF.ConclusionWith appropriate parameter values,MAF-RBFNN is able to extract the latency and amplitude of SEP from the extremely noisy background rapidly and reliably without averaging.

Objective Dipole source analysis was employed to investigate the transient changes in brain mechanisms at earlier latencies.Methods Fourteen healthy volunteers were recruited in this research and evoked event-related potentials (ERPs) of unimodal and bimodal visual auditory stimuli were recorded by 64-electrodes electroencephalograph (EEG) recording system.All these earlier phases of the stimuli were divided into several subphases by specific time window for source analysis.Results The results showed that ERPs sources were mainly generated from visual and audio cortex,and there were changes in the location and strength of the dipole sources in each sub-phase.Conclusions The result of this study implies a serial processing of sensory information in human cortices in early phase of visual and auditory stimuli.

Objective To investigate the cognitive difference between uni-modal (V,A) and bi-modal (VA)target stimuli from both vision and audition,and then to study the neural mechanisms of bi-modal enhancement.Methods This experiment adopted a speeded target stimuli detection task, both behavioral and electroencephalographic responses to uni-modal and bi-modal target stimuli which were combined from visual and auditory target stimuli,were recorded from 14 normal subjects using a 64-channel EEG NeuroScan system.The differences of cognitive between uni-modal and bi-modal stimulus were tested from both behavioral (reaction time (RT) and error rate (ER)) and event-related potentials (ERPs) (P2 latency and amplitude,P3 latency and amplitude)data,and the correlation between behavioral and ERPs results were analyzed.Results As a result,the RT,ER and P3 latency has significant difference between uni-modal and bi-modal target stimuli.In addition,there were significant correlation between behavioral data and P3 latency,especially from the RT and P3 latency.Conclusion By comparing the difference between uni-modal and bi-modal from both behavioral and ERPs results,we could reached the conclusion that the neural mechanism of bi-modal target detection was predominant over that of vision and audition uni-modal target detection,the enhancement take place not only involved in early ERP components (such as P1 and N1),but engaged at the late ERP components (such as P2 and P3).

Objective To improve overall value of healthcare industry through setting up critical inpatient medical services strategic plan.Methods Identify major objectives which the local government expects to achieve through strategic map; Standardize inpatient output and assign weight to each group through diagonosis related groups; Translate the objectives of strategic map and result of diagnosis related group to Balanced Score-card; Finally build up strategic map and according action plans.ResultsPreliminarily established 16 objectives、23 measures and 13 tasks in four perspectives including customer,internal work flow,learning and growing and finance.ConclusionThe strategy map and the balanced score-card can help implement full strategic plan of regional inpatient medical services; DRGs is a core management tool of patient-centred service output management; Balanced Score-card is able to realize continuous improvement of Beijing inpatient medical services from macro to micro persoetive.

Objective The single-trial extraction method of evoked potential has been one of the problems in EEG information processing field.According to the characteristics of somatosensory evoked electroencephalogram (EEG) with low signal-to-noise ratio and large parameter variation between trials,a novel single-trial extraction method for evoked potentials was proposed.This method aims to further improve the accuracy and characteristics of the single-trial extraction algorithm,preserve more dynamic characteristics between trials,and improve the estimation accuracy.Methods Based on wavelet filtering and multiple linear analysis,a new single-trial extraction method for EEG P300 parameters was proposed by applying the adaptive dynamic feature library.Four groups of wavelet filtered evoked EEG data were randomly selected,and used to build the feature library using overlapping average method and principal component analysis.For the single-trial extracted EEG data,the component with the highest correlation coefficient related with the current data was selected as the independent variable from the feature library,and the relevant multiple linear regression analysis was conducted.The single-trial evoked potential signal was reconstructed by the regression analysis results,in which the key features such as latency and amplitude were automatically extracted.Results Compared with the benchmark values determined by experts,the proposed algorithn can obtain more accurate estimation values of latency and amplitude in P300 components.The average difference of latency and amplitude by the proposed algorithm is (11.16±8.60) ms and (1.40±1.34)μV,respectively.These two values obtained by the proposed algorithm are much closer to that obtained by the commonly used overlapping average method of (23.26±25.76) ms and (2.52±2.50) μV,respectively.These results show that the proposed algorithm has significant advantages comparing with the traditional multiple linear regression analysis algorithm.Conclusions The dynamic updating principal component sample library of EEG data was applied to wavelet filtering and multiple linear regression,thus the dynamic characteristics were effectively preserved,and the accuracy of parameter estimation was improved.

Objective To investigate the characteristics of brain network based on brain electrical activity induced by somatosensory electrical stimulation,and to provide a theoretical basis for further understanding the mechanism of brain neural plasticity induced by somatosensory electrical stimulation.Methods Ten healthy subjects were selected and a somatosensory electrical stimulation experiment was constructed based on the directed transfer function (DTF).In the experiment,the DTF causal connection matrixes of the 32-channel EEG data of Delta,Theta,Alpha and Beta bands were obtained under the somatosensory electrical target and non-target stimulation,and the differences of clustering coefficient and global efficiency between two stimulation states were contrasted based on graph theory.Results Under the target stimulation and non-target stimulation states,the regions with stronger DTA causal connections were mainly concentrated in FCz,Cz,CPz and Pz channels.The causal connection intensity under target stimulation state was greater than that of non-target stimulation.Also,in the Delta,Theta,and Alpha bands,the clustering coefficient under the target stimulation state was significantly higher than that in the non-target stimulation state (P＜0.05).In the Delta and Theta bands,the global efficiency of the target stimulation state was significantly higher than that of the non-target stimulation state (P＜0.05).Conclusions Somatosensory electrical stimulation can activate and induce EEG brain networks.In the target stimulation state,the role of the parietal lobe in the EEG causal network is enhanced,which helps to induce attention to specific brain region plasticity,and thus realizing the nerve rehabilitation in the brain regions of interest.While in the non-target stimulation state,the synergistic interactions between brain regions were enhanced,which helps to activate and induce a wide range of associations in the whole brain network,so as to promote the global neural activity in the brain.

Brain-controlled wheelchair (BCW) is one of the important applications of brain-computer interface (BCI) technology. The present research shows that simulation control training is of great significance for the application of BCW. In order to improve the BCW control ability of users and promote the application of BCW under the condition of safety, this paper builds an indoor simulation training system based on the steady-state visual evoked potentials for BCW. The system includes visual stimulus paradigm design and implementation, electroencephalogram acquisition and processing, indoor simulation environment modeling, path planning, and simulation wheelchair control, etc. To test the performance of the system, a training experiment involving three kinds of indoor path-control tasks is designed and 10 subjects were recruited for the 5-day training experiment. By comparing the results before and after the training experiment, it was found that the average number of commands in Task 1, Task 2, and Task 3 decreased by 29.5%, 21.4%, and 25.4%, respectively ( < 0.001). And the average number of commands used by the subjects to complete all tasks decreased by 25.4% ( < 0.001). The experimental results show that the training of subjects through the indoor simulation training system built in this paper can improve their proficiency and efficiency of BCW control to a certain extent, which verifies the practicability of the system and provides an effective assistant method to promote the indoor application of BCW.