no code implementations • 30 May 2024 • Mohammad Kalbasi, MohammadAli Shaeri, Vincent Alexandre Mendez, Solaiman Shokur, Silvestro Micera, Mahsa Shoaran
Advancements in neural engineering have enabled the development of Robotic Prosthetic Hands (RPHs) aimed at restoring hand functionality.
no code implementations • 13 May 2024 • Mahsa Shoaran, Uisub Shin, MohammadAli Shaeri
Integrating smart algorithms on neural devices presents significant opportunities for various brain disorders.
no code implementations • 7 May 2024 • Cong Ding, Mingxiang Gao, Anja K. Skrivervik, Mahsa Shoaran
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 data-rate, fully integrated IR-UWB transmitter that employs a novel co-designed power amplifier (PA) and antenna interface for enhanced performance.
no code implementations • 29 Oct 2023 • Yasemin Engur, Mahsa Shoaran
The measured quiescent current is as low as 3uA and 50uA for the 0-500uA and 5-15mA load current ranges, respectively.
no code implementations • 28 Oct 2023 • Arman Zarei, Bingzhao Zhu, Mahsa Shoaran
Here, we propose to enhance the seizure detection performance by learning informative embeddings of the EEG signal.
1 code implementation • 10 May 2023 • Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, Mahsa Shoaran
The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data.
no code implementations • 29 Aug 2022 • Xiaorang Guo, MohammadAli Shaeri, Mahsa Shoaran
A challenge for future-generation implantable BMIs is to build a spike detector that features both low hardware cost and high performance.
no code implementations • 3 Jul 2022 • Uisub Shin, Cong Ding, Virginia Woods, Alik S. Widge, Mahsa Shoaran
Growing evidence suggests that phase-locked deep brain stimulation (DBS) can effectively regulate abnormal brain connectivity in neurological and psychiatric disorders.
no code implementations • 12 May 2022 • Uisub Shin, Cong Ding, Bingzhao Zhu, Yashwanth Vyza, Alix Trouillet, Emilie C. M. Revol, Stéphanie P. Lacour, Mahsa Shoaran
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.
no code implementations • 4 Apr 2022 • MohammadAli Shaeri, Arshia Afzal, Mahsa Shoaran
In this brief, we will review the emerging opportunities of on-chip AI for the next-generation implantable brain-machine interfaces (BMIs), with a focus on state-of-the-art prosthetic BMIs.
no code implementations • Journal of Neural Engineering 2022 • Lin Yao, Bingzhao Zhu, Mahsa Shoaran
In this work, we introduce the use of Riemannian-space features and temporal dynamics of electrocorticography (ECoG) signal combined with modern machine learning (ML) tools to improve the motor decoding accuracy at the level of individual fingers.
1 code implementation • NeurIPS 2021 • 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.
no code implementations • 13 Sep 2021 • 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 developing novel neuromodulation therapies to restore lost functions.
no code implementations • 28 Feb 2021 • Bingzhao Zhu, Mahsa Shoaran
Modern machine learning tools have shown promise in detecting symptoms of neurological disorders.
no code implementations • 15 Oct 2020 • 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 disorders.
no code implementations • 14 Jun 2020 • Bingzhao Zhu, Masoud Farivar, Mahsa Shoaran
We trained the resource-efficient oblique tree with power-efficient regularization (ResOT-PE) on three neural classification tasks to evaluate the performance, memory, and hardware requirements.