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Meeting Abstract

Machine-Learning Methods for Decoding Intentional Brain States

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Hill,  NJ
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Fulltext (public)

Hill-BIOMAG2010-MLBCI_[0].pdf
(Any fulltext), 9MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Hill, N. (2010). Machine-Learning Methods for Decoding Intentional Brain States. Frontiers in Neuroscience, 2010(Conference Abstract: Biomag 2010). doi:10.3389/conf.fnins.2010.06.00252.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C0DE-8
Abstract
Brain-computer interfaces (BCI) work by making the user perform a specific mental task, such as imagining moving body parts or performing some other covert mental activity, or attending to a particular stimulus out of an array of options, in order to encode their intention into a measurable brain signal. Signal-processing and machine-learning techniques are then used to decode the measured signal to identify the encoded mental state and hence extract the useramp;amp;lsquo;s initial intention. The high-noise high-dimensional nature of brain-signals make robust decoding techniques a necessity. Generally, the approach has been to use relatively simple feature extraction techniques, such as template matching and band-power estimation, coupled to simple linear classifiers. This has led to a prevailing view among applied BCI researchers that (sophisticated) machine-learning is irrelevant since “it doesnamp;amp;lsquo;t matter what classifier you use once your features are extracted.” Using examples from our own MEG and EEG experiments, Iamp;amp;lsquo;ll demonstrate how machine-learning principles can be applied in order to improve BCI performance, if they are formulated in a domain-specific way. The result is a type of data-driven analysis that is more than “just” classification, and can be used to find better feature extractors.