Research On Computation Indices For Cognitive Control Detection And Its Application

When dealing with the contradiction and distraction during a certain task,human brain has the capabilities to respond flexibly,and one of such capabilities is typically called cognitive control.For the task,cognitive control processes the related information and suppresses irrelevant cues.In this sense,cognitive control is one of the most important capability of human brain,and it is closely related with brain disease,emotional disorder and neurolysis.Although the research on cognitive control has quite a long history,to the best of the author's knowledge,there are few feasible methods for detecting and evaluating the capability of cognitive control,which is mainly caused by the following reasons: the cognitive control experiments are confined to quite limited number of paradigms,the cognitive control mechanism and the relevant computation indices are not well established,and additionally,there is no real-time approach to cognitive control detection.To establish the experiment system for evaluating cognitive control,this dissertation collects the EEG data produced during cognitive control tasks to explore the rules and mechanisms of cognitive control.On this basis,this dissertation proposes computation indices for cognitive control capability evaluation,extracted cognitive control-related feature from each single trial EEG,and constructs cognitive control classification method according to the feature space distribution of EEG samples.This dissertation focuses on the computation indices and detection of cognitive control,and the main contribution are as follows:Firstly,computation indices and the corresponding detection method for cognitive control were proposed.Based on the cognitive theory,this dissertation systematically presented biological indices for cognitive control,and proposed a cognitive control model.Additionally,the existing paradigms,which are a cognitive control experiment for non-acoustical modality under unattended situations and an auditory cognitive control paradigm under preattentive stage based on the Chinese speech stimuli,were improved,and the relevant testbed for cognitive control was designed.Through analyzing the EEG data,the method for calculating the indices of cognitive control capability evaluation was proposed,and a model for the cognitive control was established.Besides,on the basis of the previous research,this dissertation proposed a method which can detect and recognize cognitive control from a single trial EEG,so that online evaluation of cognitive control is enabled.Secondly,to solve the problem that the cognitive control mechanism triggers spontaneous processing of non-acoustical complicated information under unattended condition,this dissertation proposed the computation scheme for the calculation of cognitive control for unattended emontional information(UAEI).Using the semantic faces as stimuli,we designed a paradigm for UAEI cognitive control.Using the UAEI-ERD/ERS to calculate the brain oscillations indices related to cognitive control.Then,the average UAEI-ERD values across the respective electrode sites for each time interval were entered into repeated measures analysis of variance(ANOVA).The experimental results indicated more computation indices for UAEI cognitive control.The current work firstly constructs UAEI computation indices——?1HU AEI-ERD(UAEI-ERD of ?1 for happy expression)and ?2SU AEI-ERD(UAEI-ERD of ?2 for sad expression).The study solves the problem of UAEI cognitive control evaluation,and provides conclusions and basis for future cognitive control of acoustic channel.Thirdly,this dissertation proposed the research method for computing cognitive control quantitative indices for attentive auditory conflicting information(AACI),which are missing in the previous research.Both of existing work pay little attention to acoustical cognitive control,especially to the experimental paradigms using Chinese stimuli,and such situation motivates the authors to design,using the Stroop paradigm,we designed a paradigm for AACI cognitive control.As well as AACI-ERP(AACI-event-related potential)processing method to process the sampled and preprocessed EEG.Then we proposed the method of computing indices of AACI cognitive control,including: SCI(Sensory computation indices),ICI(Identification computation indices),ECI(Execution computation indices).A smaller AACI-ERP statistical method evidenced the validity of the proposed indices.The experimental results presented in this dissertation lead to a series of significant quantitative indices,and revealed the difference between the cognitive control under the consistent and inconsistent conditions.Finally,based on the the computation indices,this dissertation proposed an elaborate 3-stage cognitive control model which depicts the mechanism of AACI cognitive control.The study improves the evaluation methods of auditory cognitive control.Fourthly,this dissertation proposed a feature extraction method,which is able to extract features related to cognitive control from a single trial EEG data.Based on the research on cognitive control,a cognitive control disorder detection problem is presented.Then through the stimulation caused by both the conflicting and non-conflicting speech,this dissertation analyzed EEG signal and proposed the computation indices related to temporal cognitive rules of acoustical cognitive control.Additionally,this dissertation studied the feature extraction for single trial EEG sample based on cognitive control mechanism of speech confliction.Based on the computation indices relevant to AACI and 3-stage cognitive control model,each EEG sample for a single trial was divided into three parts.Average amplitude in the time-domain and Lempel-Ziv Complexity(LZC)was computed for the divided part of each EEG sample and the union of the feature of the three stages was used as the feature of each auditory cognitive EEG sample(Computing the feature of auditory single trial EEG,CFAST).The experimental results indicated that,for the single trial auditory cognitive control feature extraction,the combination of average amplitude and LZC can achieve the best accuracy(99.33%),hence the proposed method can effectively detect auditory cognitive control in single trial EGG,which enables real-time evaluation of brain cognitive control.Fifthly,a SVM pattern classifier,which take into consideration the sample distribution,is proposed to adapt the cognitive control classification problem of EEG signal.It is well known that traditional SVM suffers from bias when dealing with unevenly distributed samples,which raises the upper bound of error rate for some class.To solve the previous problem,this dissertation proposed a SVM classifier based on the sample distribution.The proposed classifier does not increase or reduce samples,and the key idea is to make the members of the minority classes farther from the hyperplane than those of the majority classes to lower the upper bound of error rate.The experimental results conducted on the UCI dataset and cognitive control EEG dataset demonstrated that the proposed classifier can effectively classify the cognitive control EEG signal.