Functional Mesh Model with Temporal Measurements for Brain Decoding

Date

2015-08-29

Author

Onal, Itir
Ozay, Mete
Yarman Vural, Fatoş Tunay

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

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We propose a method called Functional Mesh Model with Temporal Measurements (FMM-TM) to estimate a functional relationship among voxels using temporal data, and employ these relationships for brain decoding. For each sample, we measure Blood Oxygenation Level Dependent (BOLD) responses from each voxel, and construct a functional mesh around each voxel (called seed voxel) with its nearest neighbors selected using distance metrics namely Pearson correlation, cosine similarity and Euclidean distance. Then, we represent the BOLD response of a seed voxel in terms of linear combination of BOLD responses of its p-nearest neighbors. The relationship between the seed voxel and its neighbors is represented using a set of weights which are estimated by employing linear regression. We train Support Vector Machine and k-Nearest Neighbor classifiers using the estimated weights as features. We test our model in an event-related design experiment, namely object recognition, and observe that our features perform better than raw voxel intensity values, features obtained using various pairwise distance metrics, and local mesh model features extracted using stationary and temporal measurements.

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URI

https://hdl.handle.net/11511/53372

Conference Name

37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

I. Onal, M. Ozay, and F. T. Yarman Vural, “Functional Mesh Model with Temporal Measurements for Brain Decoding,” presented at the 37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Milan, ITALY, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53372.