46 citations · 176 across the 25 of their papers we have counts for
25 papers
A Memory-Efficient Retrieval Architecture for RAG-Enabled Wearable Medical LLMs-Agents
Zhipeng Liao, Kunming Shao, Jiangnan Yu +5
With powerful and integrative large language models (LLMs), medical AI agents have demonstrated unique advantages in providing personalized medical consultations, continuous health…
Repetitive TMS-based Identification of Methamphetamine-Dependent Individuals Using EEG Spectra
Ziyi Zeng, Yun-Hsuan Chen, Xurong Gao +8
The impact of repetitive transcranial magnetic stimulation (rTMS) on methamphetamine (METH) users' craving levels is often assessed using questionnaires. This study explores the fe…
CSDformer: A Conversion Method for Fully Spike-Driven Transformer
Yuhao Zhang, Chengjun Zhang, Di Wu +2
Spike-based transformer is a novel architecture aiming to enhance the performance of spiking neural networks while mitigating the energy overhead inherent to transformers. However,…
Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection
Chengjun Zhang, Yuhao Zhang, Jie Yang +1
Spiking Neural Networks (SNNs), inspired by the brain, are characterized by minimal power consumption and swift inference capabilities on neuromorphic hardware, and have been widel…
Neuro-MoBRE: Exploring Multi-subject Multi-task Intracranial Decoding via Explicit Heterogeneity Resolving
Di Wu, Yifei Jia, Siyuan Li +3
Neurophysiological decoding, fundamental to advancing brain-computer interface (BCI) technologies, has significantly benefited from recent advances in deep learning. However, exist…
NeuroCLIP: A Multimodal Contrastive Learning Method for rTMS-treated Methamphetamine Addiction Analysis
Chengkai Wang, Di Wu, Yunsheng Liao +10
Methamphetamine dependence poses a significant global health challenge, yet its assessment and the evaluation of treatments like repetitive transcranial magnetic stimulation (rTMS)…
Towards Unified Neural Decoding with Brain Functional Network Modeling
Di Wu, Linghao Bu, Yifei Jia +12
Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; how…
SynDCIM: A Performance-Aware Digital Computing-in-Memory Compiler with Multi-Spec-Oriented Subcircuit Synthesis
Kunming Shao, Fengshi Tian, Xiaomeng Wang +12
Digital Computing-in-Memory (DCIM) is an innovative technology that integrates multiply-accumulation (MAC) logic directly into memory arrays to enhance the performance of modern AI…
Towards Homogeneous Lexical Tone Decoding from Heterogeneous Intracranial Recordings
Di Wu, Siyuan Li, Chen Feng +4
Recent advancements in brain-computer interfaces (BCIs) have enabled the decoding of lexical tones from intracranial recordings, offering the potential to restore the communication…
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG
Yankun Xu, Junzhe Wang, Yun-Hsuan Chen +4
An accurate and efficient epileptic seizure onset detection can significantly benefit patients. Traditional diagnostic methods, primarily relying on electroencephalograms (EEGs), o…
A 137.5 TOPS/W SRAM Compute-in-Memory Macro with 9-b Memory Cell-Embedded ADCs and Signal Margin Enhancement Techniques for AI Edge Applications
Xiaomeng Wang, Fengshi Tian, Xizi Chen +7
In this paper, we propose a high-precision SRAM-based CIM macro that can perform 4x4-bit MAC operations and yield 9-bit signed output. The inherent discharge branches of SRAM cells…
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…
A 97 fJ/Conversion Neuron-ADC with Reconfigurable Sampling and Static Power Reduction
Jinbo Chen, Hui Wu, Jie Yang +1
A bio-inspired Neuron-ADC with reconfigurable sampling and static power reduction for biomedical applications is proposed in this work. The Neuron-ADC leverages level-crossing samp…
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…
Recent Trends and Future Prospects of Neural Recording Circuits and Systems: A Tutorial Brief
Jinbo Chen, Mahdi Tarkhan, Hui Wu +3
Recent years have seen fast advances in neural recording circuits and systems as they offer a promising way to investigate real-time brain monitoring and the closed-loop modulation…
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…
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…