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dc.contributor.authorFahad, Albalawi
dc.contributor.authorAlshehri, Sultan
dc.contributor.authorChahid, Abderrazak
dc.contributor.authorLaleg-Kirati, Taous-Meriem
dc.date.accessioned2020-05-11T15:37:14Z
dc.date.available2020-05-11T15:37:14Z
dc.identifier.urihttp://hdl.handle.net/10754/662798
dc.description.abstractPredicting human cognitive tasks from their corresponding functional Magnetic Resonance Imaging (fMRI) data is very challenging. The difficulty of this prediction problem can be summarized in two points first: the size of the dataset is very small due to the small number of subjects (i.e., patients) who can contribute to these research-based experiments, second: the size of feature vector is very large compared to the few number of the samples that can be used to derived a prediction model. One possible way to overcome these obstacles is to develop a feature generation methodology that can result a small-sized and descriptive feature vector that may improve the overall performance of the cognitive state classification problem. Motivated by these considerations, we proposes a novel feature generation methodology termed voxel weightbased (VW) features that can represent the voxels intensity when a subject is performing a certain cognitive task. This feature generation technique can project the high-dimensional feature vector into a two-dimensional feature domain. After generating the VW-based feature set, a logistic regression model (LRM) is utilized to distinguish between two cognitive states that correspond to two distinct tasks (whether a subject is viewing a picture or a sentence). To demonstrate the efficacy of the proposed feature generation scheme, a benchmark fMRI dataset is utilized to assess the performance of the LRM under the proposed features.
dc.subjectMachine Learning
dc.subjectPosition Weight Matrix
dc.subjectClassification
dc.subjectFeature Generation
dc.titleCOGNITIVE STATE PREDICTION VIA TWO-DIMENSIONAL FEATURE VECTOR
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)
refterms.dateFOA2020-05-11T15:37:14Z


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