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  • The electroencephalogram (EEG) represents an e?cient technique to measure and record brain electrical activity. The most common use of EEG includes the monitoring and diagnosis of the brain states and their disorders. It is based on the search of characteristic patterns in EEG signals and their evaluation. In terms of signal processing it uses feature analysis, more speci?cally feature extraction and classi?cation of signal components. The paper deals with the feature study of EEG signals by the self-organizing neural network (SOM). The SOM is an unsupervised method using a neighborhood function to preserve the topological properties of the input space. Resulting algorithm was implemented in MATLAB with many optional parameters that provide possibility to adjust the model to user's equirements. The graphical user interface was designed as well. General problems of feature analysis, such as extraction of appropriate characteristic features or evaluation of quality of clusters, were also discussed. Presented methodology can be generally applied to each problem requiring feature analysis.
  • The electroencephalogram (EEG) represents an e?cient technique to measure and record brain electrical activity. The most common use of EEG includes the monitoring and diagnosis of the brain states and their disorders. It is based on the search of characteristic patterns in EEG signals and their evaluation. In terms of signal processing it uses feature analysis, more speci?cally feature extraction and classi?cation of signal components. The paper deals with the feature study of EEG signals by the self-organizing neural network (SOM). The SOM is an unsupervised method using a neighborhood function to preserve the topological properties of the input space. Resulting algorithm was implemented in MATLAB with many optional parameters that provide possibility to adjust the model to user's equirements. The graphical user interface was designed as well. General problems of feature analysis, such as extraction of appropriate characteristic features or evaluation of quality of clusters, were also discussed. Presented methodology can be generally applied to each problem requiring feature analysis. (en)
Title
  • Feature analysis of EEG signals using SOM
  • Feature analysis of EEG signals using SOM (en)
skos:prefLabel
  • Feature analysis of EEG signals using SOM
  • Feature analysis of EEG signals using SOM (en)
skos:notation
  • RIV/60461373:22340/11:43882847!RIV12-MSM-22340___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S, Z(MSM6046137306)
http://linked.open...iv/cisloPeriodika
  • 2
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 199464
http://linked.open...ai/riv/idVysledku
  • RIV/60461373:22340/11:43882847
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • EEG, feature extraction, classification, SOM (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • SK - Slovenská republika
http://linked.open...ontrolniKodProRIV
  • [F57310B519B0]
http://linked.open...i/riv/nazevZdroje
  • Posterus
http://linked.open...in/vavai/riv/obor
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http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 4
http://linked.open...iv/tvurceVysledku
  • Gráfová, Lucie
  • Procházka, Aleš
  • Vyšata, Oldřich
http://linked.open...n/vavai/riv/zamer
issn
  • 1338-0087
number of pages
http://localhost/t...ganizacniJednotka
  • 22340
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