Recherche

Afficher tous les résultats pour

Accueil

Personne

Mahsa Shoaran

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (53)

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

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

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

A 41.2 nJ/class, 32-Channel On-Chip Classifier for Epileptic Seizure Detection

Mahsa Shoaran

A 41.2 nJ/class, 32-channel, patient-specific onchip classification architecture for epileptic seizure detection is presented. The proposed system-on-chip (SoC) breaks the strict energy-area-delay trade-off by employing area and memoryefficient techniques. ...

IEEE2018

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

Alexandre Schmid, Yusuf Leblebici, Mahsa Shoaran, Amirhossein Shahshahani, Masoud Shahshahani

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

Compact low-power recording architecture for multichannel acquisition of biological signals and method for compressing said biological signal data

Alexandre Schmid, Mahdad Hosseini Kamal, Mahsa Shoaran

The present invention relates to a method for compressing signal data of a plurality of biological signals captured by a plurality of implanted measurement devices. The present invention further relates to an implantable sensor configured to carry out said ...

2016

A Fully-Integrated IC with 0.85-uW/Channel Consumption for Epileptic iEEG Detection

Alexandre Schmid, Mahsa Shoaran

Feature extraction from a multichannel compressed neural signal is introduced in this brief. Compressive sensing (CS) is an efficient method for reducing the transmission data rate of sparse biological signals and lowering the power consumption of resource ...

Ieee-Inst Electrical Electronics Engineers Inc2015

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

Yusuf Leblebici, Seyed Armin Tajalli, Mahsa Shoaran

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

Low-Power Circuit and System Design for Epileptic Seizure Detection at High Spatiotemporal Resolution

Mahsa Shoaran

Smart and miniaturized implantable microsystems with diagnostic and therapeutic capabilities are becoming increasingly important for patients suffering from neurological disorders such as epilepsy. Recent developments in microfabrication technology have pr ...

EPFL2015

Structured Sampling and Recovery of iEEG Signals

Yusuf Leblebici, Volkan Cevher, Mahsa Shoaran, Cosimo Aprile, Luca Baldassarre

Wireless implantable devices capable of monitoring the electrical activity of the brain are becoming an important tool for understanding, and potentially treating, mental diseases such as epilepsy and depression. Compressive sensing (CS) is emerging as a p ...

2015

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

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

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

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

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

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

In-Vivo Validation of a Compact Inductively-Powered Neural Recording Interface

Catherine Dehollain, Alexandre Schmid, Mahsa Shoaran, Gürkan Yilmaz

2014

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

Alexandre Schmid, Hossein Afshari, Mahsa Shoaran

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

Compressive Multichannel Cortical Signal Recording

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

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

Page 3 sur 3