Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study
Data(s) |
01/02/2013
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Resumo |
Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation. |
Identificador | |
Publicador |
International Association of Computer Science and Information Technology (I A C S I T) |
Relação |
DOI:10.7763/IJMLC.2013.V3.287 Choupan, Jeiran, Hocking, Julia, Johnson, Kori, Reutens, David, & Yang, Zhengyi (2013) Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study. International Journal of Machine Learning and Computing, 3(1), pp. 132-136. |
Fonte |
Faculty of Law; School of Psychology & Counselling |
Palavras-Chave | #080106 Image Processing #110999 Neurosciences not elsewhere classified #170203 Knowledge Representation and Machine Learning #Brain Decoding #Functional MRI #Neural network #Support vector machine #conditional random field |
Tipo |
Journal Article |