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Sensors 2016, 16(3), 336; doi:10.3390/s16030336

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals

1
Laboratorio de Sistemas Bioinspirados, Departamento de Ingeniería Electrónica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico
2
Departamento de Arte y Empresa, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico
3
Cuerpo Académico de Telemática, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico
4
Laboratorio de Procesamiento Digital de Señales, Departamento de Ingeniería Electrónica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico
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Departamento de Ingeniería Electrónica, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 27 January 2016 / Revised: 26 February 2016 / Accepted: 29 February 2016 / Published: 5 March 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2155 KB, uploaded 5 March 2016]   |  

Abstract

Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states. View Full-Text
Keywords: quaternion-based signal analysis (QSA); electroencephalography (EEG); motor imagery; brain-computer interface (BCI) quaternion-based signal analysis (QSA); electroencephalography (EEG); motor imagery; brain-computer interface (BCI)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Batres-Mendoza, P.; Montoro-Sanjose, C.R.; Guerra-Hernandez, E.I.; Almanza-Ojeda, D.L.; Rostro-Gonzalez, H.; Romero-Troncoso, R.J.; Ibarra-Manzano, M.A. Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals. Sensors 2016, 16, 336.

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