16 citations · 55 across the 22 of their papers we have counts for
23 papers
RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning
Jinhan Liu, Mahsa Shoaran
Affective and cognitive disorders manifest as distributed, time-varying brain network dynamics across regions, channels, and time, challenging robust representation learning from E…
BrainDistill: Implantable Motor Decoding with Task-Specific Knowledge Distillation
Yuhan Xie, Jinhan Liu, Xiaoyong Ni +11
Transformer-based neural decoders with large parameter counts, pre-trained on large-scale datasets, have recently outperformed classical machine learning models and small neural ne…
BiND: A Neural Discriminator-Decoder for Accurate Bimanual Trajectory Prediction in Brain-Computer Interfaces
Timothee Robert, MohammadAli Shaeri, Mahsa Shoaran
Decoding bimanual hand movements from intracortical recordings remains a critical challenge for brain-computer interfaces (BCIs), due to overlapping neural representations and nonl…
rETF-semiSL: Semi-Supervised Learning for Neural Collapse in Temporal Data
Yuhan Xie, William Cappelletti, Mahsa Shoaran +1
Deep neural networks for time series must capture complex temporal patterns, to effectively represent dynamic data. Self- and semi-supervised learning methods show promising result…
Machine-Learning-Powered Neural Interfaces for Smart Prosthetics and Diagnostics
MohammadAli Shaeri, Jinhan Liu, Mahsa Shoaran
Advanced neural interfaces are transforming applications ranging from neuroscience research to diagnostic tools (for mental state recognition, tremor and seizure detection) as well…
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.…
Linear Attention for Efficient Bidirectional Sequence Modeling
Arshia Afzal, Elias Abad Rocamora, Leyla Naz Candogan +5
Linear Transformers and State Space Models have emerged as efficient alternatives to softmax Transformers for causal sequence modeling, enabling parallel training via matrix multip…
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…
A 49.8mm2 Fully Integrated, 1.5m Transmission-Range, High-Data-Rate IR-UWB Transmitter for Brain Implants
Cong Ding, Mingxiang Gao, Anja K. Skrivervik +1
To address the challenge of extending the transmission range of implantable TXs while also minimizing their size and power consumption, this paper introduces a transcutaneous, high…
A 0.21-ps FOM Capacitor-Less Analog LDO with Dual-Range Load Current for Biomedical Applications
Yasemin Engur, Mahsa Shoaran
This paper presents an output capacitor-less low-dropout regulator (LDO) with a bias switching scheme for biomedical applications with dual-range load currents. Power optimization…
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…
XTab: Cross-table Pretraining for Tabular Transformers
Bingzhao Zhu, Xingjian Shi, Nick Erickson +3
The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data. However, most existing tabular self-su…
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…
A 16-Channel Low-Power Neural Connectivity Extraction and Phase-Locked Deep Brain Stimulation SoC
Uisub Shin, Cong Ding, Virginia Woods +2
Growing evidence suggests that phase-locked deep brain stimulation (DBS) can effectively regulate abnormal brain connectivity in neurological and psychiatric disorders. This letter…
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…
Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs
MohammadAli Shaeri, Arshia Afzal, Mahsa Shoaran
Neuroscience and neurotechnology are currently being revolutionized by artificial intelligence (AI) and machine learning. AI is widely used to study and interpret neural signals (a…
Tree in Tree: from Decision Trees to Decision Graphs
Bingzhao Zhu, Mahsa Shoaran
Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree…
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…
Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection
Bingzhao Zhu, Mahsa Shoaran
Modern machine learning tools have shown promise in detecting symptoms of neurological disorders. However, current approaches typically train a unique classifier for each subject.…
Closed-Loop Neural Interfaces with Embedded Machine Learning
Bingzhao Zhu, Uisub Shin, Mahsa Shoaran
Neural interfaces capable of multi-site electrical recording, on-site signal classification, and closed-loop therapy are critical for the diagnosis and treatment of neurological di…
ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification
Bingzhao Zhu, Masoud Farivar, Mahsa Shoaran
Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT device…