Multi-Scale convolutional neural networks integrated with self-attention for motor imagery EEG decoding [0.03%]
一种结合自注意力机制的多尺度CNN解码运动想象EEG信号的方法
Shutong Duan,Penghai Li,Ding Yuan et al.
Shutong Duan et al.
Brain-computer interface (BCI), as a cutting-edge technology with great application prospects, has received widespread attention in recent years. Motor imagery (MI) electroencephalography (EEG) classification is a key component of brain-com...
Low-power analog and mixed-signal circuit techniques for next-generation miniature implantable neural interface systems [0.03%]
下一代微型植入式神经接口系统的低功耗模拟和混合信号电路技术
Linran Zhao,Yaoyao Jia
Linran Zhao
Miniature implantable neural interface devices are increasingly critical for both neuroscience research and clinical neuromodulation applications. However, device miniaturization imposes stringent constraints on power, area, and performance...
Advances in semiconductor materials and device architectures for biomedical systems: a mini review [0.03%]
用于生物医学系统的半导体材料和器件结构的进展：综述
Basharat Hussain,Abid Ullah,Israr Ali et al.
Basharat Hussain et al.
The latest improvements in semiconductor engineering have made it possible for the medical field to take advantage of the new capabilities of sensing, diagnosing, neural modulating, and monitoring therapeutics, among others. The merging of ...
A Multi-perception fusion using shared-control method for brain-mobile robot [0.03%]
一种基于共享控制的多感知融合脑控移动机器人方法
Chenyang Wang,Mengfan Li,Pengfei Zhang et al.
Chenyang Wang et al.
For human-robot collaboration, brain-computer interface is promising to express human perception to improve the adaptability of human-robot collaboration in complex environments. In this study, a multi-perception fusion using shared control...
SSA-DCNet: a cross-session MI-EEG classification network based on deformable convolution and spatial-shift attention [0.03%]
基于可变形卷积和空间移位注意力的跨会话MI-EEG分类网络(SSA-DCNet)
Xiuli Du,Hanxing Wang,Meiling Xi et al.
Xiuli Du et al.
Brain-computer interfaces (BCIs) based on motor imagery (MI) electroencephalogram (EEG) signals have shown tremendous potential in neurorehabilitation due to their non-invasive acquisition and ease of use. However, the cross-session nature ...
Advanced silicon nanomembrane based bioelectronics for flexible and stretchable implantable systems [0.03%]
基于先进硅纳米膜的柔性可拉伸植入式生物电子学
Junseok Lee,Yena Lee,Hanbi Woo et al.
Junseok Lee et al.
As the paradigm of modern medicine shifts toward prevention and management, the importance of implantable electronics for real-time physiological monitoring and therapeutic intervention has surged, yet the mechanical mismatch between conven...
Biodegradable capacitive sensors for biomedical applications: sensitivity and lifetime [0.03%]
用于生物医学应用的可降解电容式传感器：灵敏度和使用寿命
Minki Hong,Seunghun Han,Gilmo Kim et al.
Minki Hong et al.
Biodegradable implantable and wearable biomedical sensors have attracted growing attention as a promising alternative to conventional permanent electronic devices, offering transient functionality that eliminates the need for secondary surg...
Molybdenum disulfide based nanohybrids: insights into their role in electrochemical cancer biomarker detection [0.03%]
基于二硫化钼的纳米复合材料：在电化学癌症生物标志物检测中的作用研究
Naveen Thanjavur,Young-Joon Kim
Naveen Thanjavur
Cancer remains a leading cause of global mortality, where early detection and continuous monitoring are critical for improving therapeutic outcomes. However, conventional diagnostic techniques suffer from high cost, long assay times, invasi...
Enhanced intraparietal phase-lag synchronization in the high-gamma band during retention of visuospatial working memory [0.03%]
顶叶高γ频段工作记忆保持期间的增强相位滞后同步性
Jimin Park,Sangjun Lee,Seonghun Park et al.
Jimin Park et al.
Although zero-phase lag between cortical regions has been generally regarded as the optimal state, it has also been suggested that a non-zero phase delay of electroencephalography (EEG) signals in the gamma frequency band between bilateral ...
Prediction of mental fatigue states based on the CNN-LSTM architecture for BCG signals [0.03%]
基于CNN-LSTM架构的BCG信号精神疲劳状态预测方法研究
Liu Zhichao,Liu Ziqi,Li Xin
Liu Zhichao
To achieve feature fusion of ballistocardiogram (BCG) signals for the purpose of predicting mental fatigue states, a prediction model based on convolutional neural network-long short-term memory (CNN-LSTM) architecture is built in this pape...