16 citations · 55 across the 22 of their papers we have counts for
8 papers · 1 filter
MT-NAM: An Efficient and Adaptive Model for Epileptic Seizure Detection
Arshia Afzal, Volkan Cevher, Mahsa Shoaran
Enhancing the accuracy and efficiency of machine learning algorithms employed in neural interface systems is crucial for advancing next-generation intelligent therapeutic devices.…
REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates
Arshia Afzal, Grigorios Chrysos, Volkan Cevher +1
EEG-based seizure detection models face challenges in terms of inference speed and memory efficiency, limiting their real-time implementation in clinical devices. This paper introd…
Hardware-Efficient EMG Decoding for Next-Generation Hand Prostheses
Mohammad Kalbasi, MohammadAli Shaeri, Vincent Alexandre Mendez +3
Advancements in neural engineering have enabled the development of Robotic Prosthetic Hands (RPHs) aimed at restoring hand functionality. Current commercial RPHs offer limited cont…
Intelligent and Miniaturized Neural Interfaces: An Emerging Era in Neurotechnology
Mahsa Shoaran, Uisub Shin, MohammadAli Shaeri
Integrating smart algorithms on neural devices presents significant opportunities for various brain disorders. In this paper, we review the latest advancements in the development o…
Enhancing Epileptic Seizure Detection with EEG Feature Embeddings
Arman Zarei, Bingzhao Zhu, Mahsa Shoaran
Epilepsy is one of the most prevalent brain disorders that disrupts the lives of millions worldwide. For patients with drug-resistant seizures, there exist implantable devices capa…
An Accurate and Hardware-Efficient Dual Spike Detector for Implantable Neural Interfaces
Xiaorang Guo, MohammadAli Shaeri, Mahsa Shoaran
Spike detection plays a central role in neural data processing and brain-machine interfaces (BMIs). A challenge for future-generation implantable BMIs is to build a spike detector…
NeuralTree: A 256-Channel 0.227-J/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC
Uisub Shin, Cong Ding, Bingzhao Zhu +5
Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. Whil…
Closed-Loop Neural Prostheses with On-Chip Intelligence: A Review and A Low-Latency Machine Learning Model for Brain State Detection
Bingzhao Zhu, Uisub Shin, Mahsa Shoaran
The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for develop…