Author

Nima Bigdely-Shamlo

Teradata (United States)ORCID 0000-0001-6403-892XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Assay:electronic health records· 3 Approach:statistical model· 2 Approach:benchmark study· 1

Frequent Co-Authors

Christian Andreas KotheiD· 3 shared Jonathan TouryaniD· 3 shared Kay A. RobbinsiD· 3 shared Tim R. MulleniD· 3 shared Alejandro OjedaiD· 2 shared

Activity by Year

2019-2020 · peak 2/yr

Co-Author Collaboration Network

5 top collaborators · 10 mutual co-author links

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3 shared papersNeuroscience

Christian Andreas KotheORCID

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3 papers

Recent·Most cited

How Sensitive are EEG Results to Preprocessing Methods: A Benchmarking Study

Kay A. Robbins, Jonathan Touryan, Tim R. Mullen, Christian Andreas Kothe, Nima Bigdely-Shamlo

This study benchmarks EEG preprocessing methods by analyzing their impact on signal and event-related measures across multiple studies.

IEEE Transactions on Neural Systems and Rehabilitation Engineering · Mar 26, 2020Peer-reviewedNeuroscience

Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies

Nima Bigdely-Shamlo, Jonathan Touryan, Alejandro Ojeda, Christian Andreas Kothe, Tim R. Mullen, Kay A. Robbins

This paper performs a large-scale EEG mega-analysis across 18 studies, showing that automated processing and consistent metadata enable channel- and source-level insights into spectral and amplitude characteristics.

NeuroImage · Nov 23, 2019Peer-reviewedNeuroscience

Automated EEG mega-analysis II: Cognitive aspects of event related features

Nima Bigdely-Shamlo, Jonathan Touryan, Alejandro Ojeda, Christian Andreas Kothe, Tim R. Mullen, Kay A. Robbins

This paper performs a large-scale EEG analysis across 17 studies to identify cognitive aspects of event-related responses using HED tags and RSA, showing significant similarities in annotated features across diverse conditions.

NeuroImage · Sep 4, 2019Peer-reviewedNeuroscience