Abstract
A comparative evaluation is performed on two databases using three feature extraction techniques and five classification methods for a motor imagery paradigm based on Mu rhythm. In order to extract the features from electroencephalographic signals, three methods are proposed: independent component analysis, Itakura distance and phase synchronization. The last one consists of: phase locking value, phase lag index and weighted phase lag index. The classification of the extracted features is performed using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance based on classifier, the k-nearest neighbors and support vector machine. The aim of this comparison is to evaluate which feature extraction method and which classifier is more appropriate in a motor brain computer interface paradigm. The results suggest that the effectiveness of the feature extraction method depends on the classification method used.
Highlights
Brain Computer Interface (BCI) provides a new communication method for people who are suffering of motor disabilities [1]
Mu rhythm represents an oscillation of the EEG signal in the frequency band 8-12 Hz and it is affected by movements and movement imagery [2]
The used methods are described in detail in [24]-[26]. There are presented both comparisons between some features extraction methods and comparisons between some classification methods used for EEG signals recorded in a BCI motor task paradigm
Summary
Brain Computer Interface (BCI) provides a new communication method for people who are suffering of motor disabilities [1]. The electroencephalography (EEG) records the electrical activity by using electrodes placed on the scalp. Motor imagery produces reliable and distinct features in the brain activity that can be used by BCI systems. When a user performs a mental activity as left/right hand movement imagination without physically executing the movements, changes called event related desynchronizations (ERD) and event related synchronizations (ERS) appear in the sensorimotor area in the corresponding signal power of Mu or beta rhythms. Mu rhythm represents an oscillation of the EEG signal in the frequency band 8-12 Hz and it is affected by movements and movement imagery [2]. There are different features extraction methods for EEG signals suited to discriminate the motor tasks in a BCI paradigm. The independent component analysis [3], [4], Itakura distances [5]-[7] and phase synchronization methods [8]-[10] are chosen in order to be used for classification with linear discriminant analysis [11], quadratic discriminant analysis [12], Mahalanobis distance [13], the k-nearest neighbors [14], [15] and support vector machine [16], [17]
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