Functional Magnetic Resonance Imaging (fMRI) has established the field of brain decoding, meaning the prediction of the task that a subject is performing in the MRI scanner, given the corresponding images. This has been quite successful, especially when attempting discrimination between the representations of two or four distinct stimuli across the brains of multiple subjects. However, there are currently only a few studies that deal with ways to improve the scalability of existing brain decoding methodologies, in order for the resulting classifiers to be able to discriminate among tens or hundreds of possible stimuli. Such advances have potential for the creation of rigorous brain-computer interfaces, which could establish a solid communication channel with people in a vegetative state. In this work, I propose and evaluate a series of methods leading to the development of a new data-driven, scalable brain decoding framework that will enable better stimulus discrimination. The methods include: (1) A novel inter-subject spatial feature selection method that can be run using the native brain images of each subject directly, and which is not sensitive to differences in the morphology of the brain of each subject. (2) Three novel data-driven feature selection methods that use statistical association metrics in order to select regions that exhibit similar behaviour across-subjects over the course of a given experiment. The methods aim to promote enhanced exploratory power and are not susceptible to region-specific variations of the haemodynamic response function. (3) Two novel data-driven temporal denoising algorithms that can be used to improve the signal-to-noise ratio of any given task-related fMRI image and which do not impose constraints in either the experimental design nor the nature of the involved stimuli. (4) A thorough evaluation of four intensity normalisation techniques that are commonly used for across-subjects and across-sessions decoding, in order to determine their applicability for across-datasets decoding. (5) A novel feature compression and information recovery method that aims at lowering the system memory requirements for training and testing a large-scale brain decoding model using multiple datasets simultaneously.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:676773 |
| Date | January 2014 |
| Creators | Markides, Loizos |
| Contributors | Gillies, Duncan |
| Publisher | Imperial College London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10044/1/28082 |
Page generated in 0.0044 seconds