84 citations · 222 across the 14 of their papers we have counts for
15 papers
Shorter Latency of Real-time Epileptic Seizure Detection via Probabilistic Prediction
Yankun Xu, Jie Yang, Wenjie Ming +2
Although recent studies have proposed seizure detection algorithms with good sensitivity performance, there is a remained challenge that they were hard to achieve significantly sho…
Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond
Di Wu, Jie Yang, Mohamad Sawan
Machine learning on electromyography (EMG) has recently achieved remarkable success on a variety of tasks, while such success relies heavily on the assumption that the training and…
A Compact Online-Learning Spiking Neuromorphic Biosignal Processor
Chaoming Fang, Ziyang Shen, Fengshi Tian +2
Real-time biosignal processing on wearable devices has attracted worldwide attention for its potential in healthcare applications. However, the requirement of low-area, low-power a…
SpikeSEE: An Energy-Efficient Dynamic Scenes Processing Framework for Retinal Prostheses
Chuanqing Wang, Chaoming Fang, Yong Zou +2
Intelligent and low-power retinal prostheses are highly demanded in this era, where wearable and implantable devices are used for numerous healthcare applications. In this paper, w…
Binary Single-dimensional Convolutional Neural Network for Seizure Prediction
Shiqi Zhao, Jie Yang, Yankun Xu +1
Nowadays, several deep learning methods are proposed to tackle the challenge of epileptic seizure prediction. However, these methods still cannot be implemented as part of implanta…
An Event-Driven Compressive Neuromorphic System for Cardiac Arrhythmia Detection
Jinbo Chen, Fengshi Tian, Jie Yang +1
Wearable electrocardiograph (ECG) recording and processing systems have been developed to detect cardiac arrhythmia to help prevent heart attacks. Conventional wearable systems, ho…
Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures
Yankun Xu, Jie Yang, Mohamad Sawan
Epilepsy is a chronic neurological disorder affecting 1\% of people worldwide, deep learning (DL) algorithms-based electroencephalograph (EEG) analysis provides the possibility for…
Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining
Di Wu, Siyuan Li, Jie Yang +1
Deep learning associated with neurological signals is poised to drive major advancements in diverse fields such as medical diagnostics, neurorehabilitation, and brain-computer inte…
Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation
Di Wu, Jie Yang, Mohamad Sawan
Objective. Deep neural networks (DNNs) have shown unprecedented success in various brain-machine interface applications such as epileptic seizure prediction. However, existing appr…
CSP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction
Di Wu, Yi Shi, Ziyu Wang +2
Recent development in brain-machine interface technology has made seizure prediction possible. However, the communication of large volume of electrophysiological signals between se…
An End-to-End Deep Learning Approach for Epileptic Seizure Prediction
Yankun Xu, Jie Yang, Shiqi Zhao +2
An accurate seizure prediction system enables early warnings before seizure onset of epileptic patients. It is extremely important for drug-refractory patients. Conventional seizur…
Cascade Decoders-Based Autoencoders for Image Reconstruction
Honggui Li, Dimitri Galayko, Maria Trocan +1
Autoencoders are composed of coding and decoding units, hence they hold the inherent potential of high-performance data compression and signal compressed sensing. The main disadvan…
A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction
Ziyu Wang, Jie Yang, Mohamad Sawan
Accurate prediction of epileptic seizures allows patients to take preventive measures in advance to avoid possible injuries. In this work, a novel convolutional neural network (CNN…
A New Neuromorphic Computing Approach for Epileptic Seizure Prediction
Fengshi Tian, Jie Yang, Shiqi Zhao +1
Several high specificity and sensitivity seizure prediction methods with convolutional neural networks (CNNs) are reported. However, CNNs are computationally expensive and power hu…
Spatial Resolution of Local Field Potential Signals in Macaque V4
Armin Najarpour Foroushani, Sujaya Neupane, Pablo De Heredia Pastor +2
A main challenge for the development of cortical visual prostheses is to spatially localize individual spots of light, called phosphenes, by assigning appropriate stimulating param…