Mesh Learning for Object Classification using fMRI Measurements

Date

2013-09-18

Author

Ekmekci, Ömer
Ozay, Mete
Oztekin, Ilke
GİLLAM, İLKE
Oztekin, Uygar

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Machine learning algorithms have been widely used as reliable methods for modeling and classifying cognitive processes using functional Magnetic Resonance Imaging (fMRI) data. In this study, we aim to classify fMRI measurements recorded during an object recognition experiment. Previous studies focus on Multi Voxel Pattern Analysis (MVPA) which feeds a set of active voxels in a concatenated vector form to a machine learning algorithm to train and classify the cognitive processes. In most of the MVPA methods, after an image preprocessing step, the voxel intensity values are fed to a classifier to train and recognize the underlying cognitive process. Sometimes, the fMRI data is further processed for de-noising or feature selection where techniques, such as Generalized Linear Model (GLM), Independent Component Analysis (ICA) or Principal Component Analysis are employed. Although these techniques are proved to be useful in MVPA, they do not model the spatial connectivity among the voxels.

Subject Keywords

Functional Magnetic Resonance Imaging (FMRI);, Feature extraction, Machine learning, Brain decoding, Classification, Multi Voxel Pattern Analysis (MVPA)

URI

https://hdl.handle.net/11511/54519

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

Ö. Ekmekci, M. Ozay, I. Oztekin, İ. GİLLAM, and U. Oztekin, “Mesh Learning for Object Classification using fMRI Measurements,” 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54519.