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Deep brain stimulation (DBS) is an effective clinical treatment for epilepsy. However, the individualized setting and adaptive adjustment of DBS parameters are still facing great challenges. This paper investigates a data-driven hardwarein-the-loop (HIL) experimental system for closed-loop brain stimulation system individualized design and validation. The unscented Kalman filter (UKF) is utilized to estimate critical parameters of neural mass model (NMM) from the electroencephalogram recordings to reconstruct individual neural activity. Based on the reconstructed NMM, we build a digital signal processor (DSP) based virtual brain platform with real time scale and biological signal level scale. Then, the corresponding hardware parts of signal amplification detection and closed-loop controller are designed to form the HIL experimental system. Based on the designed experimental system, the proportionalintegral controller for different individual NMM is designed and validated, which proves the effectiveness of the experimental system. This experimental system provides a platform to explore neural activity under brain stimulation and the effects of various closed-loop stimulation paradigms. Index Terms-Deep brain stimulation, data drive, neural mass model, unscented Kalman filter, hardware-in-the-loop

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In this paper, we propose a tree trunk and obstacle detection method in a semistructured apple orchard environment based on improved YOLOv5s, with an aim to improve the real-time detection performance. The improvement includes using the K-means clustering algorithm to calculate anchor frame and adding the Squeeze-and-Excitation module and 10% pruning operation to ensure both detection accuracy and speed. Images of apple orchards in different seasons and under different light conditions are collected to better simulate the actual operating environment. The Gradient-weighted Class Activation Map technology is used to visualize the performance of YOLOv5s network with and without improvement to increase interpretability of improved network on detection accuracy. The detected tree trunk can then be used to calculate the traveling route of an orchard carrier platform, where the centroid coordinates of the identified trunk anchor are fitted by the least square method to obtain the endpoint of the next time traveling rout. The mean average precision values of the proposed model in spring, summer, autumn, and winter were 95.61%, 98.37%, 96.53%, and 89.61%, respectively. The model size of the improved model is reduced by 13.6 MB, and the accuracy and average accuracy on the test set are increased by 5.60% and 1.30%, respectively. The average detection time is 33 ms, which meets the requirements of real-time detection of an orchard carrier platform.

Deep brain stimulation (DBS) has proven to be an effective treatment for Parkinson's disease (PD). Adaptive control strategies offer the potential to improve efficacy, limit side effects and save battery consumption via reducing the total amount of stimulation delivered. However, the mechanisms underlying the beneficial effects of DBS for PD remain poorly understood and are still under debate, which has hindered the development of closed-loop DBS. And during the chronically implanted phase, adaptive DBS needs to be further improved to maintain its advantages. In the design of new adaptive DBS, more insights into inaccuracies when establishing mathematical basal ganglia model, unknown external disturbance signal and dynamics of focal area should be considered. A controlled auto-regressive moving average is used as the representative description of stimulus-response relationship based on recursive extended least square method, where stimulation signal is applied to subthalamic nucleus (STN) and the feedback signal is selected as local field potential signal from globus pallidus (GPi). The generalized minimum variance algorithm is used for online update of stimulation frequency and amplitude in a closed-loop manner.Simulation results illustrated the efficiency of the proposed closed-loop stimulation methods in disrupting the aberrant beta band oscillation and restore normal firing pattern when compared with the PD state. Robustness of the adaptive algorithm was further verified through dynamic change of illness state.