Author

William F Speier

University of California, Los AngelesORCID 0000-0002-0890-8684Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:electronic health records· 2 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Organism:human· 1

Frequent Co-Authors

Aria FallahiD· 2 shared Hiroki NariaiiD· 2 shared Jerome EngeliD· 2 shared Raman SankariD· 2 shared Richard J. StabaiD· 2 shared Shaun A. HussainiD· 2 shared Tonmoy MonsooriD· 2 shared Vwani RoychowdhuryiD· 2 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersNeuroscience

Aria FallahORCID

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2 papers

Recent·Most cited

PyHFO: Lightweight Deep Learning-poweredEnd-to-End High-Frequency Oscillations AnalysisApplication

Yipeng Zhang, Lawrence Liu, Yuanyi Ding, Xin Chen, Tonmoy Monsoor, Atsuro Daida, Shingo Oana, Shaun A. Hussain, Raman Sankar, Aria Fallah, Cesar Santana‐Gomez, Jerome Engel, Richard J. Staba, William F Speier, Jianguo Zhang, , Vwani Roychowdhury

PyHFO is a lightweight deep learning platform for efficient HFO detection in EEG data, achieving 50x speed improvements over standard methods.

Journal of Neural Engineering · May 9, 2024Peer-reviewedNeuroscience

Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach

Yipeng Zhang, Qiujing Lu, Tonmoy Monsoor, Shaun A. Hussain, Joe X Qiao, Noriko Salamon, Aria Fallah, Myung‐Shin Sim, Eishi Asano, Raman Sankar, Richard J. Staba, Jerome Engel, William F Speier, Vwani Roychowdhury, 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.

Brain Communications · Nov 1, 2021Peer-reviewedNeuroscience