Author
University of California, Los AngelesORCID 0000-0002-9703-0964Unclaimed ORCID profile · follow to get notified of new preprints
6 papers
Recent·Most cited
João Pedro Carvalho, Vinícius Rezende Carvalho, Eduardo Mazoni Andrade Marçal Mendes…·Lindy B. Comstock
The paper presents two EEG datasets for speech decoding, focusing on phoneme and word classification and articulatory properties, with conditions involving transcranial magnetic stimulation.
Yipeng Zhang, Lawrence Liu, Yuanyi Ding…·Vwani Roychowdhury
PyHFO is a lightweight deep learning platform for efficient HFO detection in EEG data, achieving 50x speed improvements over standard methods.
Uros Topalovic, Sam Barclay, Chenkai Ling…·Dejan M. Markovic
This paper introduces a wearable closed-loop neuromodulation system for recording single-neuron and local field potential activity in freely-moving humans.
Mitchell C. Krawczyk, Jillian R. Haney, Lin Pan…·Ye Zhang
This study identifies transcriptomic signatures of human astrocytes across age, sex, and disease states, revealing changes in synaptic function, ionic homeostasis, and tumor microenvironment interactions.
Lindy B. Comstock, Claudia S. M. Lainscsek, Vinícius Rezende Carvalho…·Terrence J. Sejnowski A B
This paper explores methods to improve EEG speech decoding by combining transcranial magnetic stimulation with delay differential analysis.
Yipeng Zhang, Qiujing Lu, Tonmoy Monsoor…·Hiroki Nariai
This paper develops a deep learning algorithm to detect and classify epileptogenic high-frequency oscillations (eHFOs) from intracranial EEG data with high accuracy.