In the remaining dataset, a ceiling effect was observed which probably precluded a clear distinction between the two classification approaches. The ensemble of classifiers systematically outperformed a single classifier for the two most challenging datasets. Ensembles of GNB and k-NN base classifiers were tested. The method was applied to three empirical fMRI datasets from multiple subjects performing visual tasks with four classes of stimuli.
The output for each individual stimulus is therefore obtained from the corresponding classifier and the final classification is achieved by simply selecting the best score. MRI scanners use strong magnetic fields, magnetic field gradients, and radio waves to generate images of the organs in the body. I have noticed that dcm2bids (which I use), does not offer the freedom of choosing whether to incorporate the scaling factor or not, there are standard settings that always include the scaling factor. Each classifier in the ensemble has a favorite class or stimulus and uses an optimized feature set for that particular stimulus. Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes of the body. However, I am uncertain about the value or necessity of using a so-called scaling factor. Therefore, we propose in this paper using an ensemble of classifiers for decoding fMRI data. Since, in many cases, different stimuli activate different brain areas, it makes sense to use a set of classifiers each specialized in a different stimulus. To address these difficulties, the majority of current approaches uses a single classifier. The problem is aggravated in the case of multiple subject experiments because of the high inter-subject variability in brain function. However, the high dimensionality and intrinsically low signal to noise ratio (SNR) of fMRI data poses great challenges to such techniques. Decoding perceptual or cognitive states based on brain activity measured using functional magnetic resonance imaging (fMRI) can be achieved using machine learning algorithms to train classifiers of specific stimuli.