Feature Analysis Of Steady-state Visual Evoked Potential And Motor Imagery EEG And The Study Of Hybrid BCI

Brain-computer interface(BCI)is an interaction system for the brain and the computer or other electrical equipments,which has become more and more popular in the area of neural engineering,rehabilitation and brain science.According to the different ways of brain signal acquisition,BCIs are generally divided into invasive and noninvasive BCI systems,in which the steady state visual evoked potential(SSVEP)-and motor imagery-based noninvasive BCIs are most popular.The low-frequency stimulus evoked SSVEPs are easy to detect.However,the low frequency stimulus can easily cause fatigue and even the evoke of photosensitive epilepsy for those who have the potential risk.Therefore,using high frequency stimulus to improve comfortability is the future work of SSVEP based BCI study.However,the high-frequency stimulating SSVEP is hard to detect,which is the major concern.On the other hand,the event-related desynchronization and synchronization(ERD/ERS)over the sensorimotor cortex occurs when subjects imaging hands movement,which is the main feature of the motor imagery-based EEG.According to the current documents,however,the ERD/ERS phenomenon is reflected in the amplitude feature.Thus,the phase-coupling feature for this phenomenon,the meaning of the zero-phase coupling and the relationship between amplitude and phase coupling are unclear.The current motor imagery BCI requires long-time training with a relatively large number of channels involved.Therefore,how to select channels and how to enhance the task prediction ability with less channels involved are also of vital importance in motor-imagery based BCIs.Given the advantages of the high-frequency SSVEP and the motor imagery based EEG,this thesis provides a new hybrid BCI system according to the thorough feature analysis of SSVEP and motor imagery based EEG.This hybrid BCI,which is based on high frequency SSVEP and motor imagery based EEG with less channels involved,overcomes the disadvantages of the low-frequency SSVEP and the BCI illiteracy phenomenon of motor-imagery based BCI.It is able to decrease the BCI illiteracy effectively,and in the meantime increase the classification accuracy.The thesis is composed of the following four main aspects:(1)To improve the comfortability of SSVEP-BCI,a comparison study of low-frequency and high-frequency SSVEP is carried out.And a method for choosing the best reference electrode and a feature extraction method are proposed,which are suitable for the medium and high frequency stimulus evoked SSVEP.(2)According to the systematic study of the amplitude and phase-coupling feature of the trained and untrained motor-imagery related EEG,the zero-phase coupling feature is illustrated to be of important meaning,which cannot be explained by volume conduction.In addition,according to the analysis of motor-imagery based EEG by the classical source localization method,the result shows that the classical source localization method reflects the amplitude-feature-based source,but the phase-coupling method shows the sources of coupling relationship effectively.This verifies that phase-coupling method is a complement for the classical source location method.(3)According to the study of the effects of the spatial filters on amplitude and phase-coupling features,it shows that spatial filtering is able to add the coupling feature into amplitude,and in the meantime enhance the amplitude feature.On this basis,this thesis proposes a channel selection method based on phase-coupling feature,which provide method-supports for the study of portable BCI using a small number of channels.In the meantime,a novel amplitude based feature extraction method,involving phase coupling information,is proposed.When comparing this method with the classical amplitude based feature extraction and the common spatial filter(CSP)method,the result shows that this method is better than the classical feature extraction methods in motor imagery based BCI.(4)A novel hybrid BCI involving high-frequency SSVEP and motor-imagery based EEG is proposed using the feature extraction methods provided in(1)and(3).Subjects can move the cursor upward or downward by the imagery task and focusing on the high-frequency stimulus.This hybrid BCI can increase the classification accuracy by 14% and 2%,respectively,comparing with the pure motor-imagery or high-frequency SSVEP based BCI.To summarize,this thesis mainly proposes the hybrid BCI with better comfortability and generosity according to the feature analysis of the two kinds of EEG signals.The novel hybrid BCI is validated successfully based on the Neuroscan EEG acquisition system and BCI2000 software,which also provides a fundamental BCI research platform.In the meantime,the proposed rule of how to choose electrodes for the BCI,especially with a small number of electrodes involved,is of great significance for the study of portable BCI and the design of new type of electrode and EEG acquisition cap.