Sharanya Arcot Desai

ORCID: 0000-0003-4542-6905
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About
Contact & Profiles
Research Areas
  • EEG and Brain-Computer Interfaces
  • Neurological disorders and treatments
  • Neuroscience and Neural Engineering
  • Epilepsy research and treatment
  • Functional Brain Connectivity Studies
  • Neuroscience and Neuropharmacology Research
  • Neural dynamics and brain function
  • Advanced Memory and Neural Computing
  • Parkinson's Disease Mechanisms and Treatments
  • Conducting polymers and applications
  • Non-Destructive Testing Techniques
  • Ferroelectric and Negative Capacitance Devices
  • Phonocardiography and Auscultation Techniques
  • Machine Learning in Healthcare
  • Muscle activation and electromyography studies

NeuroPace (United States)
2017-2025

Georgia Institute of Technology
2010-2015

The Wallace H. Coulter Department of Biomedical Engineering
2010-2015

Emory University
2011-2013

Medtronic (United States)
2013

Implantable microelectrode arrays (MEAs) have been a boon for neural stimulation and recording experiments. Commercially available MEAs high impedances, due to their low surface area small tip diameters, which are suitable single unit activity. Lowering the electrode impedance, but preserving diameter, would provide number of advantages, including reduced voltages, artifacts improved signal-to-noise ratio. Impedance reductions can be achieved by electroplating with platinum (Pt) black,...

10.3389/fneng.2010.00005 article EN Frontiers in Neuroengineering 2010-01-01

Single neuron feedback control techniques, such as voltage clamp and dynamic clamp, have enabled numerous advances in our understanding of ion channels, electrochemical signaling, neural dynamics. Although commercially available multichannel recording stimulation systems are commonly used for studying processing at the network level, they provide little native support real-time feedback. We developed open-source NeuroRighter electrophysiology hardware software platform closed-loop with a...

10.3389/fncir.2012.00098 article EN cc-by Frontiers in Neural Circuits 2013-01-01

Subacute and long-term electrocorticographic (ECoG) changes in ambulatory patients with depth cortical strip electrodes were evaluated order to determine the length of implant effect.ECoG records assessed medically intractable epilepsy who had and/or leads implanted be treated brain-responsive stimulation. Changes total spectral power, band-limited spike rate assessed.121 participating trials RNS® System a 93994 ECoG analyzed. Significant power occurred from first second months after...

10.1016/j.clinph.2017.10.036 article EN cc-by-nc-nd Clinical Neurophysiology 2017-11-15

Find interictal electrocorticographic (ECoG) biomarkers of clinical outcomes in mesiotemporal lobe (MTL) epilepsy patients. In the NeuroPace® RNS® System trials with 256 patients, 20 MTL patients most reduction seizures at Year 7 compared to baseline (upper response quartile; −96.5% median change) and least (lower −17.4% were evaluated. Clinical ECoG features from two quartiles compared. Demographic similar upper lower quartiles. Interictal spike rate (ISR) was substantially (p < 0.0001)...

10.1016/j.clinph.2019.05.017 article EN cc-by-nc-nd Clinical Neurophysiology 2019-05-31

Brain stimulation has become a widely accepted treatment for neurological disorders such as epilepsy and Parkinson's disease. These devices not only deliver therapeutic but also record brain activity, offering valuable insights into neural dynamics. However, recordings during are often blanked or contaminated by artifact, posing significant challenges analyzing the acute effects of stimulation. To address these challenges, we propose transformer-based model, Stim-BERT, trained on large...

10.3389/frai.2025.1502504 article EN cc-by Frontiers in Artificial Intelligence 2025-02-18

Microelectrode arrays (wire diameter <50 μm) were compared to traditional macroelectrodes for deep brain stimulation (DBS). Understanding the neuronal activation volume may help solve some of mysteries associated with DBS, e.g., its mechanisms action. We used c-fos immunohistochemistry investigate in rat hippocampus caused by multi-micro- and macroelectrode stimulation. At ± 1V at 25 Hz, microelectrodes (33 μm diameter) had a radius 100 μm, which is 50% that seen 150 Macroelectrodes...

10.3389/fneng.2014.00016 article EN cc-by Frontiers in Neuroengineering 2014-06-12

Abstract Objective Understanding the acute effects of responsive stimulation (AERS) based on intracranial EEG (iEEG) recordings in ambulatory patients with drug-resistant partial epilepsy, and correlating these changes clinical seizure frequency, may help clinicians more efficiently optimize settings. Methods In implanted NeuroPace® RNS® System, iEEG spectral power following active sham periods were quantified compared within individual channels. Additionally, stimulation-induced channels...

10.1016/j.clinph.2021.03.013 article EN cc-by Clinical Neurophysiology 2021-03-31

Describe changes in clinical seizure frequency and electrophysiological data recorded patients with medically-intractable seizures periventricular nodular heterotopias (PVNH) treated the RNS® System (NeuroPace, Inc., Mountain View, CA). Clinical from eight (mean follow-up of 10.1 years) were analyzed pre- post-treatment. Chronic ambulatory electrocorticograms (ECoGs) PVNHs, hippocampus neocortex evaluated to identify earliest electrographic onset type, pattern spread, interictal...

10.1016/j.clinph.2019.04.706 article EN cc-by-nc-nd Clinical Neurophysiology 2019-05-09

Hundreds of 90-s iEEG records are typically captured from each NeuroPace RNS System patient between clinic visits. While these provide invaluable information about the patient's electrographic seizure and interictal activity patterns, manually classifying them into seizure/non-seizure activity, identifying onset channels times is an extremely time-consuming process. A convolutional neural network based Electrographic Seizure Classifier (ESC) model was developed in earlier study. In this...

10.3389/fnins.2023.1156838 article EN cc-by Frontiers in Neuroscience 2023-07-05

The objective of this study was to explore using ECoG spectrogram images for training reliable cross-patient electrographic seizure classifiers, and characterize the classifiers’ test accuracy as a function amount data. channels in ∼138,000 time-series records from 113 patients were converted RGB images. Using an unsupervised image clustering technique, manual labeling 138,000 (each with up 4 channels) completed 320 h, which is estimated 5 times faster than without clustering. For supervised...

10.3389/fnins.2021.667373 article EN cc-by Frontiers in Neuroscience 2021-06-28

Finding electrophysiological features that are similar across patients with epilepsy may facilitate identifying treatment options for one patient worked in brain activity patterns. Three non-linear iEEG (intracranial electroencephalogram) embedding methods of finding cross-patient records a large dataset were developed and compared. About 1 million from 256 drug-resistant focal onset seizures who treated prospective trials the RNS System used analyses. Data 200, 25, 31 randomly selected to...

10.3389/fdata.2022.840508 article EN cc-by Frontiers in Big Data 2022-05-20

The aim of this study was to evaluate whether transfer-learning with pre-trained deep convolutional neural networks (deep CNNs) can be used for assessing patient outcomes in epilepsy. Transfer-learning the GoogLeNet InceptionV3 CNN model on large ImageNet dataset (~1.2 million images) able differentiate upper (n=12) and lower (n=9) response quartile mesiotemporal lobe epilepsy patients NeuroPace <sup xmlns:mml="http://www.w3.org/1998/Math/MathML"...

10.1109/ner.2019.8717007 article EN cc-by 2019-03-01

Architectures capable of using an algorithm to modify actuation based on measured signals are often called "closed-loop" systems. While such systems traditionally thought rely algorithms residing in device firmware, these may also reside outside the a host processor located physically nearby, or cloud-based architecture. In order serve potentially broad array data processing modalities, we have developed application programming interface (API). The API enables access sensing and stimulation...

10.1109/ner.2013.6696014 article EN 2013-11-01

Deep Brain Stimulation (DBS) has provided remarkable relief to patients with brain disorders. Traditionally, DBS is performed through a single macroelectrode implanted at specific deep structure (like the subthalamic nucleus for Parkinson's disease). Despite its great success, little known about mechanisms of action. We propose that using several microelectrodes stimulation, instead macroelectrode, may provide advantages including reduced tissue damage and increased area activated. compare...

10.1109/embc.2012.6346879 article EN Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2012-08-01

Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at International Conference for Technology Analysis of Seizures (ICTALS 2022): (1) novel developments structural magnetic resonance imaging; (2) latest electroencephalography signal-processing applications; (3) big data development clinical tools; (4) emerging field hyperdimensional computing; (5) new generation...

10.1111/epi.17566 article EN cc-by-nc Epilepsia 2023-03-02

Objective: To assess electrographic differences between upper and lower response quartile patients treated with the RNS® System (NeuroPace, Inc.) for refractory focal epilepsies. Background: Identifying features differentiating most favorable to treatment will aid in discovering disease biomarkers endpoints. Design/Methods: 179 adults had ≥ 6 months of clinical seizure data at year 7 post-implant were included this analysis. Patients (n=45: MTL=16, Neocortical=27, MTL + neocortical=2)...

10.1212/wnl.90.15_supplement.s53.007 article EN Neurology 2018-04-10

May 9, 2019April 2019Free AccessInterictal Electrographic Features Differentiate MTL Responders And Nonresponders (S48.001)Sharanya Arcot Desai, Thomas Tcheng, and Martha MorrellAuthors Info & AffiliationsApril 2019 issue92 (15_supplement)https://doi.org/10.1212/WNL.92.15_supplement.S48.001 Letters to the Editor

10.1212/wnl.92.15_supplement.s48.001 article EN Neurology 2019-04-09
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