• Irina Knyazeva
  • Alexander Efitorov
  • Yulia Boytsova
  • Sergey Danko
  • Vladimir Shiroky
  • Nikolay Makarenko
© Springer Nature Switzerland AG 2019. In this paper, we present classification algorithms based on single-trial ElectroEncephaloGraphy (EEG) during the performance of tasks with the dominance of mental and sensory attention. Statistical data analysis showed numerous significant differences of EEG wavelet spectra density during this task at the group level. We decided to use wavelet power spectral density (PSD) computed in each channel for single trial as the source of feature extraction for the classification task. To obtain a low-dimensional representation of PSD image convolutional autoencoder (CNN) was trained. With this encoded representation binary classification for each subject with multilayer perceptron (MLP) were performed. The classification error varies depending on the subject with the average true classification rate is 83.4%, and the standard deviation is 6.6%. So this approach potentially could be used in the tasks where pattern classification is used, such as a clinical decision or in Brain-Computer Interface (BCI) system.
Original languageEnglish
Title of host publicationStudies in Computational Intelligence
Pages190-195
Number of pages6
Volume799
DOIs
StatePublished - 1 Jan 2019

Publication series

NameStudies in Computational Intelligence
PublisherSpringer
ISSN (Print)1860-949X

    Scopus subject areas

  • Artificial Intelligence

    Research areas

  • Deep learning, EEG single trial classification, Mental and sensory attention, Neural networks

ID: 35578540