Model-free optimal de-drifting and enhanced detection in fMRI data

Shah, Adnan; Seghouane, Abd-Krim

Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Model-free optimal de-drifting and enhanced detection in fMRI data

Date

2013

Authors

Shah, Adnan

Seghouane, Abd-Krim

Publisher

IEEE

Abstract

Discriminating between active and non-active brain voxels in noisy functional magnetic resonance imaging (fMRI) data plays an important role when investigating task-related activations of the neuronal sites. A novel method for efficiently capturing drifts

URI

http://hdl.handle.net/1885/82657

Collections

ANU Research Publications

Source

IEEE International Workshop on Machine Learning for Signal Processing, MLSP

Type

Conference paper

DOI

10.1109/MLSP.2013.6661963

Restricted until

2037-12-31

Downloads

File

Description

01_Shah_Model-free_optimal_de-drifting_2013.pdf (743.13 KB)

Full item page

Cultural advice

Model-free optimal de-drifting and enhanced detection in fMRI data

Date

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

Keywords

Citation

URI

Collections

Source

Type

Book Title

Entity type

Access Statement

License Rights

DOI

Restricted until

Downloads