A Novel Twist Deformation Model of Soft Tissue in Surgery Simulation

Real-time performance and accuracy are two most challenging requirements in virtual surgery training.These difficulties limit the promotion of advanced models in virtual surgery,including many geometric and physical models.This paper proposes a physical model of virtual soft tissue,which is a twist model based on the Kriging interpolation and membrane analogy.The proposed model can quickly locate spatial position through Kriging interpolation method and accurately compute the force change on the soft tissue through membrane analogy method.The virtual surgery simulation system is built with a PHANTOM OMNI haptic interaction device to simulate the torsion of virtual stomach and arm,and further verifies the real-time performance and simulation accuracy of the proposed model.The experimental results show that the proposed soft tissue model has high speed and accuracy,realistic deformation,and reliable haptic feedback.

Multiband decomposition and spectral discriminative analysis for motor imagery BCI via deep neural network

Human limb movement imagery,which can be used in limb neural disorders rehabilitation and brain-controlled external devices,has become a significant control paradigm in the domain of brain-computer interface(BCI).Although numerous pioneering studies have been devoted to motor imagery classification based on electroencephalography(EEG)signal,their performance is somewhat limited due to insufficient analysis of key effective frequency bands of EEG signals.In this paper,we propose a model of multiband decomposition and spectral discriminative analysis for motor imagery classification,which is called variational sample-long short term memory(VS-LSTM)network.Specifically,we first use a channel fusion operator to reduce the signal channels of the raw EEG signal.Then,we use the variational mode decomposition(VMD)model to decompose the EEG signal into six band-limited intrinsic mode functions(BIMFs)for further signal noise reduction.In order to select discriminative frequency bands,we calculate the sample entropy(SampEn)value of each frequency band and select the maximum value.Finally,to predict the classification of motor imagery,a LSTM model is used to predict the class of frequency band with the largest SampEn value.An open-access public data is used to evaluated the effectiveness of the proposed model.In the data,15 subjects performed motor imagery tasks with elbow flexion/extension,forearm supination/pronation and hand open/close of right upper limb.The experiment results show that the average classification result of seven kinds of motor imagery was 76.2%,the average accuracy of motor imagery binary classification is 96.6%(imagery vs.rest),respectively,which outperforms the state-of-the-art deep learning-based models.This framework significantly improves the accuracy of motor imagery by selecting effective frequency bands.This research is very meaningful for BCIs,and it is inspiring for end-to-end learning research.