Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity
Joint Authors
Pisarchik, Alexander N.
Maksimenko, Vladimir A.
Kurkin, Semen A.
Pitsik, Elena N.
Musatov, Vyacheslav Yu.
Runnova, Anastasia E.
Efremova, Tatyana Yu.
Hramov, Alexander E.
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-01
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Maksimenko, Vladimir A.& Kurkin, Semen A.& Pitsik, Elena N.& Musatov, Vyacheslav Yu.& Runnova, Anastasia E.& Efremova, Tatyana Yu.…[et al.]. 2018. Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity. Complexity،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1136712
Modern Language Association (MLA)
Maksimenko, Vladimir A.…[et al.]. Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity. Complexity No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1136712
American Medical Association (AMA)
Maksimenko, Vladimir A.& Kurkin, Semen A.& Pitsik, Elena N.& Musatov, Vyacheslav Yu.& Runnova, Anastasia E.& Efremova, Tatyana Yu.…[et al.]. Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity. Complexity. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1136712
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136712