Author

Chenqian Le

New York University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Assay:electronic health records· 3 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Adeen FlinkeriD· 3 shared Amirhossein Khalilian-GourtaniiD· 3 shared Daniel J. FriedmaniD· 3 shared Orrin DevinskyiD· 3 shared Patricia C. DuganiD· 3 shared Werner DoyleiD· 3 shared Xupeng CheniD· 2 shared Yao Wang· 2 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersNeuroscience

Adeen FlinkerORCID

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3 papers

Recent·Most cited

Prediction of Stimulation-Defined Eloquent Cortex Using Graph-Theoretical Connectivity from Electrocorticography During Presurgical Mapping

Nikasadat Emami, Andrew J. Michalak, Amirhossein Khalilian-Gourtani, Antoine Ratouchniak, Chenqian Le, Xupeng Chen, Orrin Devinsky, Werner Doyle, Patricia C. Dugan, Daniel J. Friedman, Yao Wang, Adeen Flinker

The paper uses graph-theoretical connectivity from ECoG to predict eloquent cortex during presurgical mapping, finding varying accuracy across functional domains.

bioRxiv · May 6, 2026 · PreprintNeuroscience

Neural Dynamics of Automatic Speech Production

Amirhossein Khalilian-Gourtani, Chenqian Le, Faxin Zhou, Erika Jenson, Patricia C. Dugan, Orrin Devinsky, Werner Doyle, Daniel J. Friedman, Yao Wang, Adeen Flinker

This study investigates the neural dynamics of automatic speech production using ECoG recordings, revealing a distributed cortical network with complex feedforward and feedback signal representation.

bioRxiv · Feb 11, 2026 · PreprintNeuroscience

Subject-Agnostic Transformer-Based Neural Speech Decoding from Surface and Depth Electrode Signals

Junbo Chen, Xupeng Chen, Ran Wang, Chenqian Le, Amirhossein Khalilian-Gourtani, Erika Jensen, Patricia C. Dugan, Werner Doyle, Orrin Devinsky, Daniel J. Friedman, Adeen Flinker, Yao Wang

This paper introduces a transformer-based model for speech decoding from neural signals using surface and depth electrodes, achieving high performance across multiple participants and electrode types.

bioRxiv · Mar 11, 2024 · PreprintNeuroscience