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.
Highlights
The interest in establishing direct communication between the brain and other external devices using electroencephalographic (EEG) signals has increased with the use of brain-computer interface (BCI) systems
The quaternion-based signal analysis (QSA) method method was implemented (Algorithm 1) to represent EEG signal blocks as shown in Table 4, and to was implemented (Algorithm 1) to represent EEG signal blocks as shown in Table 4, and to extract extract the features related to the cues shown
After exhaustive tests of the proposed technique, the results show that this methodology for monitoring, representing and classifying EEG signals can be usefully applied for the purposes of having individuals control external devices
Summary
The interest in establishing direct communication between the brain and other external devices using electroencephalographic (EEG) signals has increased with the use of brain-computer interface (BCI) systems. According to Wolpaw [1], “BCI interfaces allow to record the brain signals of an individual, extract their characteristics and turn them into artificial outputs that operate outside or in your own body”. These signals are processed and analyzed using various mathematical methods to gather information regarding the frequency components and, in turn, the functional relationships between brain areas. Several strategies have been presented to analyze brain signals by extracting and classifying EEG signals for cognitive-movement detection purposes. Hongyu [21] presents an on-line classification method for BCI based on common spatial patterns (CSP) for feature extraction, using support vector machine (SVM) as a classifier for imagined hand and foot movements achieving accuracy results of 86.3%, 91.8% and 92.0%
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