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Mahsa Shoaran

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Related publications (53)

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A 3 mm2, Energy-Efficient, Multi-Data-Rate, FDMA Transmitter with On-Chip Antenna for Next-Generation Neural Implants

Anja Skrivervik, Mahsa Shoaran, Mingxiang Gao, Cong Ding

This paper presents a versatile energy-efficient wireless transmitter (TX) for neural implants with high/ultrahigh channel counts. The TX supports a wide range of data transmission rates, from 50Mb/s to 1.2 Gb/s, through a novel dual-modulation approach. O ...

IEEE2024

Enhancing Epileptic Seizure Detection with EEG Feature Embeddings

Mahsa Shoaran, Bingzhao Zhu

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 capable of monitoring neural activity, promptly triggering neurostimulation to r ...

ArXiv2023

Compressive Multichannel Cortical Signal Recording

Yusuf Leblebici, Pierre Vandergheynst, Alexandre Schmid, Mahsa Shoaran, Mahdad Hosseini Kamal

This paper presents a novel approach to acquire multichannel wireless intracranial neural data based on a compressive sensing scheme. The designed circuits are extremely compact and low-power which confirms the relevance of the proposed approach for multic ...

IEEE2013

A 16-Channel 1.1mm2 Implantable Seizure Control SoC with Sub-μW/Channel Consumption and Closed-Loop Stimulation in 0.18μm CMOS

Yusuf Leblebici, Alexandre Schmid, Mahsa Shoaran

We present a 16-channel seizure detection system-on-chip (SoC) with 0.92μW/channel power dissipation in a total area of 1.1mm² including a closed-loop neural stimulator. A set of four features are extracted from the spatially filtered neural data to achiev ...

IEEE2016

Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection

Mahsa Shoaran, Bingzhao Zhu

Modern machine learning tools have shown promise in detecting symptoms of neurological disorders. However, current approaches typically train a unique classifier for each subject. This subject-specific training scheme requires long labeled recordings from ...

IEEE2021

Analysis and Characterization of Variability in Subthreshold Source-Coupled Logic Circuits

Yusuf Leblebici, Alexandre Schmid, Mahsa Shoaran, Seyed Armin Tajalli

This article explores the effect of device parameter variations on the performance of subthreshold source-coupled logic (STSCL) circuits. A test chip has been fabricated in a standard CMOS 90 nm technology to study the matching properties of STSCL circuits ...

2015

Systems and methods for energy-efficient measurement of neurophysiological oscillations

Mahsa Shoaran, Uisub Shin

A system for measuring synchrony between two or more regions, or within a single region, of a subject's brain. The system can include a signal conditioning module in communication with a phase extraction module. The signal conditioning module can receive a ...

2022

A Novel Compressive Sensing Architecture for High-Density Biological Signal Recording

Alexandre Schmid, Mahsa Shoaran, Hossein Afshari

The massive amount of data recorded by dense electrode arrays which are routinely connected to Nyquist-sampling signal conditioning blocks introduces new design challenges for implantable and wireless biological signal acquisition. Five different architect ...

2014

Adaptive Learning-Based Compressive Sampling for Low-power Wireless Implants

Yusuf Leblebici, Volkan Cevher, Catherine Dehollain, Mahsa Shoaran, Luca Baldassarre, Franco Maloberti, Cosimo Aprile, Kerim Türe, Gürkan Yilmaz

Implantable systems are nowadays being used to interface the human brain with external devices, in order to understand and potentially treat neurological disorders. The most predominant design constraints are the system’s area and power. In this paper, we ...

2018

Intelligent Neural Interfaces: An Emerging Era in Neurotechnology

Mahsa Shoaran, Uisub Shin, Mohammad Ali 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 of three categories of intelligent neural prostheses featuring embedded signa ...

2024 IEEE Custom Integrated Circuits Conference (CICC)2024

Closed-Loop Neural Interfaces with Embedded Machine Learning

Mahsa Shoaran, Bingzhao Zhu, Uisub Shin

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. However, deploying machine learning algorithms on low-power neural ...

IEEE2020

Jitter Analysis and Measurement in Subthreshold Source-Coupled Differential Ring Oscillators

Yusuf Leblebici, Mahsa Shoaran, Seyed Armin Tajalli

The jitter and the phase noise of ring oscillators utilizing subthreshold source-coupled logic (STSCL) style are analyzed in this paper. Closed-form equations are derived to predict the jitter and phase noise caused by white and flicker noise. Measurement ...

2015

A Systematic Design Flow for Compact and Efficient Analog Front-Ends in Closed-Loop Neuromodulation Systems

Mahsa Shoaran

In the growing field of closed-loop neuromodulation, designing power-and area-efficient analog front-ends (AFEs) for neural recording poses a significant challenge. Thus, an effective design flow is needed to ensure system's hardware efficiency and to auto ...

IEEE2024

Compact Low-Power Cortical Recording Architecture for Compressive Multichannel Data Acquisition

Pierre Vandergheynst, Alexandre Schmid, Mahsa Shoaran, Mahdad Hosseini Kamal

This paper introduces an area- and power-efficient approach for compressive recording of cortical signals used in an implantable system prior to transmission. Recent research on compressive sensing has shown promising results for sub-Nyquist sampling of sp ...

2014

Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs

Mahsa Shoaran, Mohammad Ali Shaeri, Arshia Afzal

Neuroscience and neurotechnology are currently being revolutionized by artificial intelligence (AI) and machine learning. AI is widely used to study and interpret neural signals (analytical applications), assist people with disabilities (prosthetic applica ...

IEEE2022

A Low Power Multi-Class Migraine Detection Processor Based on Somatosensory Evoked Potentials

Mahsa Shoaran, Bingzhao Zhu

Migraine is a disabling neurological disorder that can be recurrent and persist for long durations. The continuous monitoring of the brain activities can enable the patient to respond on time before the occurrence of the approaching migraine episode to min ...

2021

A Hardware-Efficient EMG Decoder with an Attractor-based Neural Network for Next-Generation Hand Prostheses

Silvestro Micera, Mahsa Shoaran, Solaiman Shokur, Vincent Alexandre Mendez, Mohammad Ali Shaeri

Advancements in neural engineering have enabled the development of Robotic Prosthetic Hands (RPHs) aimed at restoring hand functionality. Current commercial RPHs offer limited control through basic on/off commands. Recent progresses in machine learning ena ...

IEEE2024

Intelligent Neural Interfaces: An Emerging Era in Neurotechnology

Mahsa Shoaran, Uisub Shin, Mohammad Ali Shaeri

Over the past decade, there has been a growing interest in the development of intelligent neural interface systems-on-chip (SoCs) for a range of neurological disorders and emerging brain-machine interface (BMI) applications. The shift toward creating intel ...

IEEE2024

XTab: Cross-table Pretraining for Tabular Transformers

Mahsa Shoaran, Bingzhao Zhu

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-supervised learning models fail to leverage information across multiple data t ...

JMLR.org2023

REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates

Volkan Cevher, Mahsa Shoaran, Grigorios Chrysos, Arshia Afzal

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 introduces a novel graph-based residual state update mechanism (REST) for real-tim ...

2024