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035   $a(DE-He213)978-3-030-30581-9
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035   $a(PPN)243769504
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100   $a20190831d2020      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAnalysis and Classification of EEG Signals for Brain?Computer Interfaces /$fby Szczepan Paszkiel
205   $a1st ed. 2020.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020.
215   $a1 online resource (VI, 132 p.) 
225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v852
311   $a3-030-30580-5 
327   $aChapter 1. Introduction -- Chapter 2. Data acquisition methods for human brain activity -- Chapter 3. Brain-computer interface (BCI) technology, etc.
330   $aThis book addresses the problem of EEG signal analysis and the need to classify it for practical use in many sample implementations of brain?computer interfaces. In addition, it offers a wealth of information, ranging from the description of data acquisition methods in the field of human brain work, to the use of Moore?Penrose pseudo inversion to reconstruct the EEG signal and the LORETA method to locate sources of EEG signal generation for the needs of BCI technology. In turn, the book explores the use of neural networks for the classification of changes in the EEG signal based on facial expressions. Further topics touch on machine learning, deep learning, and neural networks. The book also includes dedicated implementation chapters on the use of brain?computer technology in the field of mobile robot control based on Python and the LabVIEW environment. In closing, it discusses the problem of the correlation between brain?computer technology and virtual reality technology.
410  0$aStudies in Computational Intelligence,$x1860-949X ;$v852
606   $aComputational intelligence
606   $aBiomedical engineering
606   $aNeurobiology
606   $aArtificial intelligence
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aBiomedical Engineering and Bioengineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T2700X
606   $aNeurobiology$3https://scigraph.springernature.com/ontologies/product-market-codes/L25066
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
615  0$aComputational intelligence.
615  0$aBiomedical engineering.
615  0$aNeurobiology.
615  0$aArtificial intelligence.
615 14$aComputational Intelligence.
615 24$aBiomedical Engineering and Bioengineering.
615 24$aNeurobiology.
615 24$aArtificial Intelligence.
676   $a003.5
676   $a612.80285
700   $aPaszkiel$b Szczepan$4aut$4http://id.loc.gov/vocabulary/relators/aut$01228634
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910484074603321
996   $aAnalysis and Classification of EEG Signals for Brain?Computer Interfaces$92852400
997   $aUNINA