Since the early 1990s, fMRI has come to dominate the brain mapping field due to its relatively low invasiveness, absence of radiation exposure, and relatively wide availability. It is widely used to get a 3-D map of brain activity, with a spatial resolution of few milliseconds. We try to employ various machine learning techniques to decode the cognitive states of a person, based on his brain fMRIs. This is particularly challenging because of the complex nature of brain and numerous interdependencies in the brain activity. We trained multiple classifiers for decoding cognitive states and analyzed the results. We also introduced a technique for considerably reducing the large dimensions of the fMRI data, thereby increasing the classification accuracy. We have compared our results with current state-of-the-art implementations, and a significant improvement in the performance was observed. We got 90% accuracy, which is significantly better than the state-of-the-art implementation. We ran our algorithm on a heterogeneous dataset containing fMRI scans from multiple persons, and still got an accuracy of 83%, which is significant since it shows our classifiers were able to identify some basic abstract underlying neural activity, which are subject-independent, corresponding to the each cognitive states.
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- Decoding Cognitive States from Brain fMRIs: The “Most Differentiating Voxels” Way
- Springer Berlin Heidelberg
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