Memorias de investigación
Research Publications in journals:
Artificial neural network classification of motor-related EEG: an increase in classification accuracy by reducing signal complexity
Year:2018

Research Areas
  • Biology and other natural sciences,
  • Physical aplications to problems and biological systems,
  • Cognitive neuroscience,
  • Motor process

Information
Abstract
We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8?13 Hz) and delta (1?5 Hz) brainwaves than in the high-frequency beta brainwave (15?30 Hz). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.
International
Si
JCR
Si
Title
Complexity
ISBN
1076-2787
Impact factor JCR
1,829
Impact info
Datos JCR del año 2017
Volume
2018
10.1155/2018/9385947
Journal number
From page
9385947-1
To page
9385947-10
Month
SIN MES
Ranking
Q1
Participants
  • Autor: Vladimir Maksimenko Yuri Gagarin State Technical University of Saratov
  • Autor: Semen Kurkin Yuri Gagarin State Technical University of Saratov
  • Autor: Elena Pitsik Yuri Gagarin State Technical University of Saratov
  • Autor: Vyacheslav Musatov Yuri Gagarin State Technical University of Saratov
  • Autor: Anastasia Runnova Yuri Gagarin State Technical University of Saratov
  • Autor: Tatyana Efremova Yuri Gagarin State Technical University of Saratov
  • Autor: Alexander Hramov Yuri Gagarin State Technical University of Saratov
  • Autor: Alexander Pisarchik UPM

Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Tecnologías para Ciencias de la Salud
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB