Research On Some Key Technologies Of Practical Hybrid Brain-Computer Interface

A Brain-Computer Interface(BCI)is a specialized type of technology that enables human-computer interaction characterized by the use of electroencephalography(EEG)signals as information carriers,which allows the human brain to exert direct control over external equipment.Research on BCI technology has important theoretical significance and broad application prospects.There are four main types of BCI systems based on EEG: steady-state visual evoked potentials(SSVEPs),motor imagery(MI),P300 based on event-related potentials(ERPs),and slow cortical potential(SCP).Different types of BCI systems have their own advantages and disadvantages.The hybrid BCI system compensates the various shortcomings of each mode by combining two or more traditional BCI modalities and can develop a more powerful and more stable multimodal BCI system.Although impressive improvements in BCI efficiency have been achieved,the current BCI systems are far from being perfect in terms of reliability and generalizability.This suboptimal performance can be mainly attributed to a low signal-to-noise ratio,the presence of artifacts in the data and the non-stationary nature of the EEG signal.This dissertation focuses on two major types of BCI: MI and SSVEP.The main goal is to improve the autonomous controllability and stability of BCI,and pay attention to the natural and comfort indicators of training and operation processes,so as to design and implement practical and online BCI systems.A series of research works were carried out around this goal from the EEG signal acquisition,signal processing,feature extraction,classification and recognition modules.The research content includes two major parts: theoretical research and system realization.The core content of theoretical research is the research of EEG signal processing and pattern recognition algorithms.In the motor imagery BCI(MI-BCI),this dissertation focuses on the spatial filtering methods of common spatial pattern(CSP)and independent component analysis(ICA).In terms of SSVEP-BCI,this dissertation focuses on the application of canonical correlation analysis(CCA)in the asynchronous BCI.To comprehensively evaluate the performance of different algorithms,we have established MI-BCI and SSVEP-BCI databases and algorithm evaluation platforms.The difficulty of system implementation is to establish a more reasonable brain-computer control strategy,study some key fusion technologies of hybrid BCI,and build online and asynchronous BCI systems.In the process of system implementation,a series of engineering problems needs to be solved,such as the development of software systems,real-time data communication,and the balance between the recognition accuracy and execution efficiency of algorithms.The contributions and innovations of this dissertation are given as follows:First,in view of the disadvantages of the traditional CSP algorithms,which are not stable and are sensitive to noise,this dissertation combined the advantages of various existing CSP algorithms from the perspective of time-frequency-space joint optimization and proposed a sliding frequency band CSP algorithm,which was used to automatically find the optimal personalized feature parameters for the specific subject.Compared with other algorithms,this method can effectively reduce the calculation cost in the detection step and facilitate the realization of a BCI system with fewer channels and lower cost.The experimental results show that the proposed algorithm has not only good classification performance but also fast execution efficiency,which meets the requirements of the online mode BCI system development.Second,to reduce the training time of the MI-BCI system and to solve the shortcoming that the traditional ICA algorithms are not easy to use,this dissertation comprehensively compared a variety of classic ICA algorithms and proposed an improved information maximization ICA algorithm by modifying the iterative strategy,ultimately applying this algorithm to online system detection.This method can automatically detect and output independent components related to motor imagery tasks from less unlabelled training data,greatly reducing the training time;in addition,the proposed algorithm has better spatial model transfer performance between different subjects,high execution efficiency,and good robustness,and the designed MI-BCI system has a stable operation state.Third,aiming at the shortcomings of the existing MI-BCI system with fewer goals and lower information transmission rate(ITR),this dissertation proposed a new multimodal BCI system based on Alpha rhythm and SSVEP.By applying the sliding window voting discrimination(SWVD)strategy and optimizing the experimental paradigm,the classical CCA algorithm was successfully applied for continuous control in an asynchronous BCI system.With the developed system,obvious improvements in the ITR and sensitivity were achieved,which will be beneficial for the development of practical BCI systems.Fourth,according to the design goals,we have designed and developed synchronous online MI-BCI systems based on CSP and ICA spatial filtering,an asynchronous MI-BCI system based on the hybrid of EEG and EOG,and an asynchronous SSVEP-BCI system with Alpha Rhythm and SSVEP.The implementation of these systems makes our BCI theoretical research results available,demonstrating that the work done in this dissertation has important application value.Finally,the work and research results of this dissertation are summarized,and the prospect of future research is put forward.