Research On Event-related Potentials (erp) Evoked By Visual-auditory Cross Stimulation Based On Number Speller

Event Related Potential (ERP) which closely correlated with the sense perceptive cognitions is an important neurophysiology paradigm for Brain Computer Interface (BCI). And in ERP, the P300 component is one of the most widely used endogenous components for BCI studies. Unfortunately, there is a difficulty of restricting condition each other between ERP recognition and evoking rate, which affects the accuracy of ERP feature extraction and its information transmitting rate badly. To try to improve the rate of ERP information transmitting and the accuracy of ERP pattern classification simultaneously, in this thesis, the visual-auditory evoking model was used instead of visual evoking model and the features of the signals were analyzed.Based on thorough investigation about the generation of ERP,the feature of P300 components and the present situation of ERP stimulation, the traditional P300 speller stimulating pattern was improved in this thesis, which was a cross model evoking experiment for brain computer interface providing corresponding semantic auditory stimulating simultaneously when visual stimulation (number 1-9) is proved. The NeuroScan 4.3 digital sampled system was used to acquire the signals we need. The subjects were divided into two groups, the first group used the traditional P300 speller pattern, while, the second group used the new visual-auditory stimulation pattern we designed.The EEG signals acquired in this system were analyzed by conventional neurophysiological analysis. Results showed that the P300 components in visual-auditory cross model processing had higher amplitudes and shorter latency compared with unimodal visual evoking, which was consistent with existing research and proves that the cross model pattern was more suitable for high-speed brain machine interface. The constrained Independent Component Analysis (cICA)was used in feature extracting to extract the most non-Gaussian components which was closely related to the reference signal. Then the resampled P300 features of some important channels combined with the independent component extracted by cICA were used as the feature vector for recognition tasks. The multiple output decision values of support vector machine were applied to build the classification algorithm, which was used to identify the target number.Results showed that visual-auditory cross stimulation can evoke better EEG feature, which can lead to a higher classification rate and lower the number of the repeated trials. Thus, the accuracy of identify the target number and its information transmitting rate can be improved simultaneously. Studies showed that using the visual-auditory cross stimulation as the model of the brain machine interface can solve the difficulty existed in unimodal stimulation and effectively improve the ability of brain machine interface.