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  Machine-Learning Methods for Decoding Intentional Brain States

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C0DE-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-B14D-0
Genre: Meeting Abstract

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 Creators:
Hill, NJ1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2010-04
 Publication Status: Published online
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: 6430
DOI: 10.3389/conf.fnins.2010.06.00252
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Title: 17th International Conference on Biomagnetism (BIOMAG 2010)
Place of Event: Dubrovnik, Croatia
Start-/End Date: 2010-03-28 - 2010-04-01
Invited: Yes

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Title: Frontiers in Neuroscience
Source Genre: Journal
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Publ. Info: Lausanne, Switzerland : Frontiers Research Foundation
Pages: - Volume / Issue: 2010 (Conference Abstract: Biomag 2010) Sequence Number: - Start / End Page: - Identifier: ISSN: 1662-4548
ISSN: 1662-453X
CoNE: https://pure.mpg.de/cone/journals/resource/1662-4548