A Survey on Machine Learning Algorithms in Little-Labeled Data for Motor Imagery-Based Brain-Computer Interfaces

The Brain-Computer Interfaces(BCIs)had been proposed and used in therapeutics for decades.However,the need of time-consuming calibration phase and the lack of robustness,which are caused by little-labeled data,are restricting the advance and application of BCI,especially for the BCI based on motor imagery(MI).In this paper,we reviewed the recent development in the machine learning algorithm used in the MI-based BCI,which may provide potential solutions for addressing the issue.We classified these algorithms into two categories,namely,and enhancing the representation and expanding the training set.Specifically,these methods of enhancing the representation of features collected from few EEG trials are based on extracting features of multiple bands,regularization,and so on.The methods of expanding the training dataset include approaches of transfer learning(session to session transfer,subject to subject transfer)and generating artificial EEG data.The result of these techniques showed the resolution of the challenges to some extent.As a developing research area,the study of BCI algorithms in little-labeled data is increasingly requiring the advancement of human brain physiological structure research and more transfer learning algorithms research.

Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network

A damage identification system of carbon fiber reinforced plastics （CFRP） structures is investigated using fiber Bragg grating （FBG） sensors and back propagation （BP） neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis （FEA）. At last, the damage identification system is verified on a CFRP plate with 300mm × 300mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.

Research on Pressure Tactile Sensing Technology Based on Fiber Bragg Grating Array

A pressure tactile sensor based on the fiber Bragg grating （FBG） array is introduced in this paper, and the numerical simulation of its elastic body was implemented by finite element software （ANSYS）. On the basis of simulation, fiber Bragg grating strings were implanted in flexible silicone to realize the sensor fabrication process, and a testing system was built. A series of calibration tests were done via the high precision universal press machine. The tactile sensor array perceived external pressure, which is demodulated by the fiber grating demodulation instrument, and three-dimension pictures were programmed to display visually the position and size. At the same time, a dynamic contact experiment of the sensor was conducted for simulating robot encountering other objects in the unknown environment. The experimental results show that the sensor has good linearity, repeatability, and has the good effect of dynamic response, and its pressure sensitivity was 0.03 nm/N In addition, the sensor also has advantages of anti-electromagnetic interference, good flexibility, simple structure, low cost and so on, which is expected to be used in the wearable artificial skin in the future.