Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Learning to Decode Cognitive States from Brain Images
Machine Learning
EEG responds to conceptual stimuli and corpus semantics
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
BI'09 Proceedings of the 2009 international conference on Brain informatics
Using SVM to predict high-level cognition from fMRI data: a case study of 4*4 sudoku solving
BI'09 Proceedings of the 2009 international conference on Brain informatics
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Parallels between machine and brain decoding
BI'12 Proceedings of the 2012 international conference on Brain Informatics
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fMRI and ERP/EEG are two different sources for scanning the brain for building mind state decoders. fMRI produces accurate images but it is expensive and cumbersome. ERP/EEG is cheaper and potentially wearable but it gives more coarse-grain data. Recently the metaphor between machines and brains has been introduced in the context of mind state decoders: the "readers for machines' thoughts". This metaphor gives the possibility for comparing mind state decoder methods in a more controlled setting. In this paper, we compare the fMRI and ERP/EEG in the context of building "readers for machines' thoughts". We want assess if the cheaper ERP/EEG can be competitive with fMRI models for building decoders for mind states. Experiments show that accuracy of "readers" based on ERP/EEG-like data are considerably lower than the one of those based on fMRI-like images.