Author

Mina Sadeghi Najafabadi

University of Connecticut

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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Approach:statistical model· 3 Assay:electronic health records· 2 Research Focus:differential expression· 1

Frequent Co-Authors

Monty A. Escabı́iD· 3 shared Ian H. StevensoniD· 2 shared Xiu ZhaiiD· 2 shared Ashley Norris· 1 shared Bin FengiD· 1 shared Fatemeh KhatamiiD· 1 shared Fengrong He· 1 shared Heather L. ReadiD· 1 shared

Activity by Year

2019-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersNeuroscience

Monty A. Escabı́ORCID

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3 papers

Recent·Most cited

NeuroscienceProceedings of the National Academy of SciencesPeer-reviewedNov 20, 2020

Distinct neural ensemble response statistics are associated with recognition and discrimination of natural sound textures

Xiu Zhai, Fatemeh Khatami, Mina Sadeghi Najafabadi…·Monty A. Escabı́

The paper shows that distinct neural response statistics in the auditory system encode sound texture features for recognition and discrimination, with fast spectrum-based discrimination and slow correlation-based recognition.

23 citations

statistical model

𝕏 PostBluesky

NeuroscienceFrontiers in NeurosciencePeer-reviewedJul 17, 2020

Optimal Multichannel Artifact Prediction and Removal for Brain Machine Interfaces and Neural Prosthetics

Mina Sadeghi Najafabadi, Longtu Chen, Kelsey Dutta…·Monty A. Escabı́

The paper develops an artifact removal algorithm for neural recordings, improving signal-to-noise ratio by 20-40 dB across multiple stimulation scenarios.

10 citations

electronic health recordsstatistical model

𝕏 PostBluesky

NeurosciencePLoS BiologyPeer-reviewedOct 1, 2019

A Neural Ensemble Correlation Code for Sound Category Identification

Mina Sadeghi Najafabadi, Xiu Zhai, Ian H. Stevenson, Monty A. Escabı́

The paper shows that correlation statistics between frequency-organized auditory channels can be used to identify sound categories with high accuracy, mirroring human performance.

29 citations

electronic health recordsstatistical model

𝕏 PostBluesky