Optimization of machine learning method combined with brain-computer interface rehabilitation system.

[Purpose] Stroke patients are unable to move on their own and must be rehabilitated to allow the nervous system to trigger and restore its function. Traditional practice is to use electrode caps to extract brain wave features and combine them with assistive devices. However, there are problems that the electrode cap is not easy to wear, and the potential recognition is not good, and different extraction methods will affect the accuracy of the Brain-Computer Interfaces (BCI), which still has room for improvement. [Participants and Methods] The brainwave headphones used in this experiment do not must a conductive gel to get a good EEG for neural induction and drive the upper limb rehabilitation robot. Next, 8 stroke patients and 200 normal participants were invited for a 4-week rehabilitation training. The effectiveness of the training was determined using Fast Fourier Transform (FFT), Magnitude squared coherence (MSC) feature extraction methods, and five machine learning techniques that induced flicker frequencies. [Results] The results show that the optimal steady-state visual evoked flicker frequency is 6 Hz, and the identification rate of FFT is about 5.2% higher than that of the MSC method. Using an optimized model for different feature extraction methods can improve the recognition rate by 1.3%-9.1%. [Conclusion] The images based on Fugl-Meyer Assessment (FMA), Modified Ashworth Scale (MAS) index improvement, and functional Magnetic Resonance Imaging (fMRI) show that the sensory region of brain movement has become a concentrated activation phenomenon. Besides strengthening the feature extraction method also lets the elbow has an obvious recovery effect.

Microstructure overlapping image application with optical decryption.

Currently, valuable tickets are scanned and verified by embedding quick response (QR) codes, but few studies have embedded encrypted information into QR codes to prevent counterfeiting. In the existing literature on color image-based QR codes, there is room for improvement in image quality and anti-counterfeiting functions. We propose an optically decrypted microstructure overlapping image technology, hiding the information points of two-dimensional barcodes on the image reservation area in the ticket, and using the optical principle to use the lenticular lens as a decryption component, to make the image produce continuous dynamic effects and increase the difficulty of forgery. In addition, the distribution of embedded parameters and differences will also affect the distortion of hidden information, which may cause the QR code to fail to read, so the median edge detection predictor is proposed for distortion control comparison. The experimental results prove that our method provides low distortion of information-hiding technology.

Implementation of interactive games to a shoulder rehabilitation and evaluation system.

BACKGROUND: After an operation, the shoulder and wrist might not be able to lift and swing freely, and must be assisted with rehabilitation training. OBJECTIVE: In this paper, Kinect combined with multiple sensors of a Bluetooth ball is proposed to improve the measurement function of the arm's micro-motion trajectory, rotation amount, and acceleration, which cannot be detected by Kinect alone. METHODS: We designed two virtual scene rehabilitation games for clinical trials. We performed validity analysis with a paired sample t-test. RESULTS: A significance value of P*< 1 was obtained, and the arm lift angle shows an improvement from 30∘ to 60∘, indicating that the range of motion of the hand and shoulder is gradually improving. CONCLUSION: Experiments show that virtual games combined with multiple sensors can better understand the patient's rehabilitation situation.