Publications by Mahsa Shoaran | EPFL Graph Search

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

The term 'brain-computer interface' (BCI) is widely used. However, even among experts, its meaning can sometimes be unclear 1-3. Lack of clarity from the term has real-world implications: it can muddle regulatory guidelines, it may confuse investors, it ca ...

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 is crucial for systems with multiple activation modes such as neural interfa ...

Mahsa Shoaran

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Compressive Multichannel Cortical Signal Recording

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

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

Systems and methods for energy-efficient measurement of neurophysiological oscillations

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

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

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

Intelligent Neural Interfaces: An Emerging Era in Neurotechnology

Closed-Loop Neural Interfaces with Embedded Machine Learning

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

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

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

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

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

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

Intelligent Neural Interfaces: An Emerging Era in Neurotechnology

XTab: Cross-table Pretraining for Tabular Transformers

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

An application-based taxonomy for brain–computer interfaces

A 0.21-ps FOM Capacitor-Less Analog LDO with Dual-Range Load Current for Biomedical Applications