7 citations · 10 across the 8 of their papers we have counts for
8 papers
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…
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…
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…