TY - GEN
T1 - Functional Mesh Model with Temporal Measurements for brain decoding
AU - Onal, Itir
AU - Ozay, Mete
AU - Vural, Fatos T.Yarman
PY - 2015/11/4
Y1 - 2015/11/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84953235274&partnerID=8YFLogxK
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U2 - 10.1109/EMBC.2015.7318930
DO - 10.1109/EMBC.2015.7318930
M3 - Conference contribution
C2 - 26736830
AN - SCOPUS:84953235274
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2624
EP - 2628
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -