Výsledky vyhledávání - "Parima Ahmadipour"

First-in-human prediction of chronic pain state using intracranial neural biomarkers

Autor: Prasad Shirvalkar, Jordan Prosky, Gregory Chin, Parima Ahmadipour, Omid G. Sani, Maansi Desai, Ashlyn Schmitgen, Heather Dawes, Maryam M. Shanechi, Philip A. Starr, Edward F. Chang

Publikováno v: Nature Neuroscience.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::07f74b170de9a0cd9789b08a80141b4c
https://doi.org/10.1038/s41593-023-01338-z

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Post-Stimulus Encoding of Decision Confidence in EEG: Toward a Brain-Computer Interface for Decision Making

Autor: Nitin Sadras, Omid G. Sani, Parima Ahmadipour, Maryam M. Shanechi

When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performan

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ebe33c1407f005919b2f2eb4ac14db1c
https://doi.org/10.1101/2022.11.01.514790

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Confidence Prediction from EEG Recordings in a Multisensory Environment

Autor: Luca Citi, Riccardo Poli, Davide Valeriani, Maryam M. Shanechi, Caterina Cinel, Nitin Sadras, Jacobo Fernandez-Vargas, Parima Ahmadipour

Publikováno v: Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology.

This paper investigates the possibility of decoding decision confidence from electroencephalographic (EEG) brain activity of human subjects during a multisensory decision-making task. In recent research we have shown that decision confidence correlat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c11a886a602de5a6811c351bc66a1b7d
https://doi.org/10.1145/3397391.3397426

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Adaptive tracking of human ECoG network dynamics

Autor: Parima Ahmadipour, Edward F. Chang, Maryam M. Shanechi, Yuxiao Yang

Publikováno v: Journal of Neural Engineering.

Objective. Extracting and modeling the low-dimensional dynamics of multi-site electrocorticogram (ECoG) network activity is important in studying brain functions and dysfunctions and for developing translational neurotechnologies. Dynamic latent stat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a40607017e37e883d227eab41023aeca
https://doi.org/10.1088/1741-2552/abae42

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Adaptive latent state modeling of brain network dynamics with real-time learning rate optimization

Autor: Maryam M. Shanechi, Yuxiao Yang, Parima Ahmadipour

Publikováno v: Journal of neural engineering. 18(3)

Objective. Dynamic latent state models are widely used to characterize the dynamics of brain network activity for various neural signal types. To date, dynamic latent state models have largely been developed for stationary brain network dynamics. How

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9bf2b5c31dd0842a37ffc5f357398faa
https://pubmed.ncbi.nlm.nih.gov/33254159

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Investigating the effect of forgetting factor on tracking non-stationary neural dynamics

Autor: Yuxiao Yang, Parima Ahmadipour, Maryam M. Shanechi

Publikováno v: NER

Neural dynamics can be non-stationary in many neural applications such as brain-machine-interfaces (BMIs) and long-term brain stimulation. Adaptive modeling is a useful approach in tracking the neural non-stationarities. Our prior work has tracked ti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c3ebde38d22e51639c2cb8069dd8eaa4
https://doi.org/10.1109/ner.2019.8717119

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