The Study Of Multi-events ERP Componments Decomposition Algorithm And Application

Event-related potentials (ERP or ERPs) are the brain electric activities related to specific event; it has a stable time locked relationship with definable reference event and can be estimated by average across many trials aligned to a specific reference event onset time point, i.e. a stimulus or a behavioral response. If two or more events are included in a single trial, the cross-contamination of ERP components evoked by these different events will exist in average ERP when aligned to the reference event onset time. Because of its high temporal resolution, ERP is widely adopted in cognitive neuroscience and clinical medicine field. However, during the research of higher level cognitive function of brain, the cross-contamination of ERP components evoked by different events may greatly influence the understanding of psychology and physiological processes corresponding to ERP components. Since this problem has not been solved, transitional average technology is still applied in many brain cognitive function researches and hence resulting in conflicting conclusions about some cognitive function. Thus, the development of a precise and effective method for extracting ERP components evoked by different events is crucial to ERP researches.In order to eliminate the cross-contamination of ERP components evoked by different events, a multi-events ERP components decomposition method as well as its application in experiments are presented and discussed in this paper.1 Decomposition method of three and more events ERP components is presented. The validity of this method was proved by simulated data.2 Considering the feature of decomposition method, researches into the influence of boundary and event time distribution on the effectiveness of decomposition are carried out. By comparing the effectiveness of algorithms in periodic boundary condition and non-periodic boundary condition, we find that better and more precise result can be obtained using periodic boundary condition rather than using non-periodic boundary condition. Comparing the results among the different event time distribution, we find the shape of event time distribution does not influence decomposition result, while the standard deviation of event time distribution has a significant influence on the decomposition result: better result could be achieved by introducing greater standard deviation of event time distribution. The study of decomposition methods can provide guidance for the design of psychological experiments.3 Multi-events ERP components decomposition algotithm based on Wiener deconvolution is presented to recover event evoked ERP components. To solve the problem of the inherent ill-posed problem of multi-events ERP components decomposition algorithm, we introduced Wiener deconvolution to regularize the ill-posed problem, and then designed the optimal filter to recover event related components by estimating signal and noise power spectral. A comparison of the decomposition results between difference regularization methods shows that Wiener deconvolution decomposition algorithm has the best decomposition effect. When applying it to actual experiment data, we analyzed the reliability of decomposition result based on the features of ERP and physiological common knowledge.4 Wiener deconvolution decomposition algorithm is applied to the researches of response inhibition to verify the long existing disagreements in this field . The preliminary results show that the NoGo-P3 effect of response inhibition comes from response related component that may be not related to response inhibition process, while NoGo-N2 effect is actually related to response inhibition.