Application Research Of Multimodal Brain-computer Interface Based On EEG & FNIRS

Brain-Computer Interface(BCI)technique enables the user to communicate and control external devices by using electrical signals detected on the scalp since it does not depend on conventional neuromuscular control.In this work,BCI is used as an effective tool to actively control rehabilitation robot as well as evaluate the performance of motor imagery(MI)to improve the functional recovery after the rehabilitation training.Several technical problems have been considered and taken into account of most-influenced factors in current clinical applications of BCI: limited signal types,low target identification accuracy as well as characteristic difference between dysfunctional patients and normal people.The main research results include:Firstly,based on the temporal resolution of EEG and fNIRS,a EEG-fNIRS hybrid BCI technique is proposed and exploded to design new experimental task.And a denoising technique based on the Ensemble Empirical mode decomposition(EEMD)is put forward to analyze the multi-modal signals simultaneously collected by combined EEG&fNIRS devices.That is,the denoised signals can be reconstructed on the basis of low frequency information and high frequency information which has been filtered out via wavelet threshold denoising method.It has been demonstrated that more useful signals have been well kept after noise reduction due to the complementary signals obtained from EEMD and wavelet threshold denoising method.Secondly,since EEG signals are severely nonlinear,a nonlinear dynamics analysis approach has been used to characterize EEG signals in terms of spatial patterns.Also taking into account of the interactions between each channel,common spatial pattern(CSP)method is employed for classification and multiple channels have been validated to provide high classification accuracies.To avoid the influence of initial value and random threshold determination strategy on the back-propagation(BP)neural networks classification algorithm,GA optimization method is applied and numerous experiments are performed to show the efficiency of the proposed algorithm.Finally,multimodality based BCI technique is applied to the rehabilitation training of disabled patients.It has been demonstrated that no obvious difference is observed between normal subjects and dysfunctional patients in the multimodal signals during the MI process.Therefore,in consideration of the complementary property between signals from normal subjects and patients,the information of normal subjects should be added as supplementary information in the design of BCI system.