This longitudinal EEG study demonstrates that integrating interactive visual gaming with conventional upper limb resistance training enhances motor recovery in post-stroke patients compared to traditional rehabilitation alone. The game-based approach resulted in superior behavioral outcomes, with significantly greater improvements in specific muscle groups. Notably, among participants in the game-based group, those who exhibited peripheral motor improvement also showed significantly greater alpha-band suppression in the lesioned motor cortex compared to non-improved individuals. EEG signals were preprocessed using Independent Component Analysis (ICA), and Power Spectral Density (PSD) analysis was performed to evaluate cortical activation during the rehabilitation period. The findings suggest a positive association between motor cortex activation and improvements in muscle strength. This study presents a clinically feasible rehabilitation model that incorporates game-based feedback without modifying standard protocols, supporting its potential as a more interactive and effective approach for early neurorehabilitation.

Stroke (Cerebrovascular Accident, CVA) is one of the diseases with the greatest impact on human health, particularly due to the neurological dysfunctions that often follow, such as upper or lower limb paralysis, foot drop, muscle weakness, and cognitive impairment, which significantly affect patients' daily lives. Existing research indicates that the first three months after a stroke is the "golden period" for treatment, during which rehabilitation training can greatly improve a patient's quality of life and promote neuroplasticity. However, some stroke patients are unable to engage in active rehabilitation training due to physical limitations and can only rely on passive rehabilitation methods. To address this issue, this study proposes a closed-loop brain-controlled exoskeleton system based on brain-computer interface (BCI) technology to enhance rehabilitation in post-stroke patients. This study designed a brain-controlled exoskeleton system consisting of open-loop control and closed-loop feedback. In the open-loop control stage, patients control the exoskeleton's standing, walking, and sitting commands through electroencephalogram (EEG) signals; in the closed-loop feedback stage, the rehabilitation effect is evaluated through brainwave analysis and clinical evaluation. Experimental results demonstrate that the system can effectively assist patients in regaining motor control during training and elicit significant neurophysiological responses in the preparation and execution stages of movement. Additionally, clinical evaluation results showed that patients made notable progress in muscle strength and functional recovery. Although the study sample size was small (n=3) and the treatment period was short, the study highlights the potential application of brain-controlled exoskeleton systems in post-stroke rehabilitation. Future studies should expand the sample size and include long-term follow-up to further assess its clinical feasibility and long-term effects.

Because of the high design complexity in multicore systems, contemporary multi-core systems usually suffer from serious thermal issues caused by the large variety of workloads. To monitor the heat phenomenon, the cost-efficient way is to allocate number-limited thermal sensors on the multicore system. Consequently, a lot of methods were proposed to find proper locations for thermal sensors allocation in the recent decade. However, due to the time-varying characteristic of the temperature behavior on the multi-core system, the temperature distribution usually changes along with time. Besides, the temperature distribution also depends on the target application on the multi-core system, which increases the difficulty to find the proper locations for the thermal sensor allocation. The improper locations for the thermal error while using the sensing information from the allocated error while using the sensing information from the allocated art, we propose an entropy-based thermal sensor allocation method, which aims to find locations to cover as many different temperature behaviors on the system as possible. In this way, we can apply the Restricted Isometry Property (RIP) to reconstruct the full-chip temperature distribution efficiently based on the number-limited thermal sensing information. Compared with the previous thermal sensor allocation methods, we can reduce the average full-chip temperature reconstruction error by 3% to 93%. In addition, the maximum error can be reduced by 3% to 96% as well.

As the complexity of the multicore system grows, the large workload diversity results in serious thermal problems. In a practical way, the number of placed thermal sensors is usually limited due to the manufacturing cost. In recent years, the compressive sensing (CS) theory is proven as an efficient way to reconstruct the original signal by using fewer sampling data. However, due to the high computational complexity during the signal reconstruction, the CS theory is not appropriate to apply to the real-time temperature monitoring in the current multicore system. In this article, we propose a grid-based sensor placement approach to placement the number-limited thermal sensors on the target multicore system. On the other hand, we adopt the matrix inversion bypass (MIB) property to reduce the computational complexity of two widely used signal reconstruction approaches in CS theory [i.e., the orthogonal matching pursuit (OMP) and stagewise OMP (StOMP)]. Due to the characteristic of random sampling in CS theory, the complexity of thermal sensor placement for multicore systems can be reduced significantly. In addition, the proposed MIB-based temperature reconstruction method helps to satisfy the requirement of real-time temperature estimation. The experimental results show that the proposed approach can reduce 57%–93% average full-system temperature reconstruction error compared with the previous non-CS-based approaches. Besides, we can also reduce 22%–41% computing latency compared with the current CS-based reconstruction algorithm. Due to the MIB-based operation, we can bypass the matrix inversion operation for temperature reconstruction. Therefore, the hardware overhead of the temperature reconstruction unit can be reduced significantly. Compared with the conventional approaches, we can reduce 24%–87% area overhead and improve 50%–220% hardware efficiency.