Learning to Decode Cognitive States from Brain Images Export

@article{Mitchell04:DecodeCognitiveStates, abstract = {Over the past decade, functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain. A typical fMRI experiment can produce a three-dimensional image related to the human subject's brain activity every half second, at a spatial resolution of a few millimeters. As in other modern empirical sciences, this new instrumentation has led to a flood of new data, and a corresponding need for new data analysis methods. We describe recent research applying machine learning methods to the problem of classifying the cognitive state of a human subject based on fRMI data observed over a single time interval. In particular, we present case studies in which we have successfully trained classifiers to distinguish cognitive states such as (1) whether the human subject is looking at a picture or a sentence, (2) whether the subject is reading an ambiguous or non-ambiguous sentence, and (3) whether the word the subject is viewing is a word describing food, people, buildings, etc. This learning problem provides an interesting case study of classifier learning from extremely high dimensional (105 features), extremely sparse (tens of training examples), noisy data. This paper summarizes the results obtained in these three case studies, as well as lessons learned about how to successfully apply machine learning methods to train classifiers in such settings.}, author = {Mitchell, Tom M. and Hutchinson, Rebecca and Niculescu, Radu S. and Pereira, Francisco and Wang, Xuerui and Just, Marcel and Newman, Sharlene}, citeulike-article-id = {995345}, citeulike-linkout-0 = {http://dx.doi.org/10.1023/B:MACH.0000035475.85309.1b}, day = {1}, doi = {10.1023/B:MACH.0000035475.85309.1b}, journal = {Machine Learning}, keywords = {distributed, fmri, notbound, quals}, month = {October}, number = {1}, pages = {145--175}, posted-at = {2006-12-14 19:37:19}, priority = {2}, title = {Learning to Decode Cognitive States from Brain Images}, url = {http://dx.doi.org/10.1023/B:MACH.0000035475.85309.1b}, volume = {V57}, year = {2004} }

TY - JOUR ID - Mitchell04:DecodeCognitiveStates L3 - citeulike-article-id:995345 N2 - Over the past decade, functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain. A typical fMRI experiment can produce a three-dimensional image related to the human subject's brain activity every half second, at a spatial resolution of a few millimeters. As in other modern empirical sciences, this new instrumentation has led to a flood of new data, and a corresponding need for new data analysis methods. We describe recent research applying machine learning methods to the problem of classifying the cognitive state of a human subject based on fRMI data observed over a single time interval. In particular, we present case studies in which we have successfully trained classifiers to distinguish cognitive states such as (1) whether the human subject is looking at a picture or a sentence, (2) whether the subject is reading an ambiguous or non-ambiguous sentence, and (3) whether the word the subject is viewing is a word describing food, people, buildings, etc. This learning problem provides an interesting case study of classifier learning from extremely high dimensional (105 features), extremely sparse (tens of training examples), noisy data. This paper summarizes the results obtained in these three case studies, as well as lessons learned about how to successfully apply machine learning methods to train classifiers in such settings. IS - 1 JF - Machine Learning EP - 175 TI - Learning to Decode Cognitive States from Brain Images VL - V57 SP - 145 KW - distributed KW - fmri KW - notbound KW - quals AU - Mitchell, Tom AU - Hutchinson, Rebecca AU - Niculescu, Radu AU - Pereira, Francisco AU - Wang, Xuerui AU - Just, Marcel AU - Newman, Sharlene PY - 2004/10/01/ UR - http://dx.doi.org/10.1023/B:MACH.0000035475.85309.1b ER -