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Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method
Joint Authors
Romero-Troncoso, R. D. J.
Batres-Mendoza, Patricia
Guerra-Hernandez, Erick I.
Almanza-Ojeda, Dora L.
Montoro-Sanjose, Carlos R.
Ibarra-Manzano, M. A.
Rostro-Gonzalez, H.
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-03
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes.
The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique.
Specifically, we can sample the signal in variable time periods (from 0.5 s to 3 s, in half-a-second intervals) to determine the relationship between the number of samples and their effectiveness in classifying signals.
In addition, to strengthen the classification process a number of boosting-technique-based decision trees were implemented.
The results show an 82.30% accuracy rate for 0.5 s samples and 73.16% for 3 s samples.
This is a significant improvement compared to the original QSA technique that offered results from 33.31% to 40.82% without sampling window and from 33.44% to 41.07% with sampling window, respectively.
We can thus conclude that iQSA is better suited to develop real-time applications.
American Psychological Association (APA)
Batres-Mendoza, Patricia& Ibarra-Manzano, M. A.& Guerra-Hernandez, Erick I.& Almanza-Ojeda, Dora L.& Montoro-Sanjose, Carlos R.& Romero-Troncoso, R. D. J.…[et al.]. 2017. Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-16.
https://search.emarefa.net/detail/BIM-1141315
Modern Language Association (MLA)
Batres-Mendoza, Patricia…[et al.]. Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-16.
https://search.emarefa.net/detail/BIM-1141315
American Medical Association (AMA)
Batres-Mendoza, Patricia& Ibarra-Manzano, M. A.& Guerra-Hernandez, Erick I.& Almanza-Ojeda, Dora L.& Montoro-Sanjose, Carlos R.& Romero-Troncoso, R. D. J.…[et al.]. Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-16.
https://search.emarefa.net/detail/BIM-1141315
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141315