PUBLICATION DATE

2016-03-10

AUTHORS

Gui-Jung Kim, Jung-Soo Han

TITLE

A method of unsupervised machine learning based on self-organizing map for BCI

ISSUE

2

VOLUME

19

ISSN (print)

1386-7857

ISSN (electronic)

1573-7543

ABSTRACT

Brain computer interface (BCI) is a technology that controls computers or machines using the thoughts or intentions of a person. EEG signal measured from the human scalp is reflected with thoughts and intentions of a person, and when using the signal processing technologies such as machine learning or pattern recognition, intentions of such users can be interpreted. This study has proposed an autonomous machine learning method applicable to BCI based on Kohonen’s self-organizing map which is one of the representative methods of unsupervised learning. To achieve this, learning area adjustment method and autonomous machine learning rules using interactive functions were proposed. Learning area adjustment and machine learning have used the side control effects according to the interactive functions based on Kohonen’s self-organizing map. After determining the winning neuron, the connection strength of neuron was adjusted according to the learning rules. As the learning area gradually decreased according to the increase in the number of learning, the flow towards the input of weighted value of output layer neuron was mitigated and an autonomous machine learning that can complete the learning as the network reaches the equilibrium state was proposed. This BCI technology that connects the brain and machines is predicted to be applicable to a variety of BCI applications in that unlike the existing manual mechanism, it is applied with person’s brainwaves.

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35 TRIPLES      30 PREDICATES      35 URIs      21 LITERALS

Subject Predicate Object
1 articles:fcc051c27c6ef70d7001efd437d2d3c5 sg:abstract Abstract Brain computer interface (BCI) is a technology that controls computers or machines using the thoughts or intentions of a person. EEG signal measured from the human scalp is reflected with thoughts and intentions of a person, and when using the signal processing technologies such as machine learning or pattern recognition, intentions of such users can be interpreted. This study has proposed an autonomous machine learning method applicable to BCI based on Kohonen’s self-organizing map which is one of the representative methods of unsupervised learning. To achieve this, learning area adjustment method and autonomous machine learning rules using interactive functions were proposed. Learning area adjustment and machine learning have used the side control effects according to the interactive functions based on Kohonen’s self-organizing map. After determining the winning neuron, the connection strength of neuron was adjusted according to the learning rules. As the learning area gradually decreased according to the increase in the number of learning, the flow towards the input of weighted value of output layer neuron was mitigated and an autonomous machine learning that can complete the learning as the network reaches the equilibrium state was proposed. This BCI technology that connects the brain and machines is predicted to be applicable to a variety of BCI applications in that unlike the existing manual mechanism, it is applied with person’s brainwaves.
2 sg:articleType OriginalPaper
3 sg:coverYear 2016
4 sg:coverYearMonth 2016-06
5 sg:ddsId s10586-016-0550-4
6 sg:ddsIdJournalBrand 10586
7 sg:doi 10.1007/s10586-016-0550-4
8 sg:doiLink http://dx.doi.org/10.1007/s10586-016-0550-4
9 sg:hasContributingOrganization grid-institutes:grid.411143.2
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11 sg:hasContribution contributions:1b878a19df9017730b3ef0d8f138c3eb
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18 sg:indexingDatabase Scopus
19 Web of Science
20 sg:issnElectronic 1573-7543
21 sg:issnPrint 1386-7857
22 sg:issue 2
23 sg:language English
24 sg:license http://scigraph.springernature.com/explorer/license/
25 sg:pageEnd 985
26 sg:pageStart 979
27 sg:publicationDate 2016-03-10
28 sg:publicationYear 2016
29 sg:publicationYearMonth 2016-03
30 sg:scigraphId fcc051c27c6ef70d7001efd437d2d3c5
31 sg:title A method of unsupervised machine learning based on self-organizing map for BCI
32 sg:volume 19
33 sg:webpage https://link.springer.com/10.1007/s10586-016-0550-4
34 rdf:type sg:Article
35 rdfs:label Article: A method of unsupervised machine learning based on self-organizing map for BCI
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