Common Spatial Pattern for the Classification of Imagined Geometric Objects

Electroencephalographic (EEG) signals contain cognitive information, which can be used by Brain-Computer Interface (BCI) systems to control devices through thought. In this work we study the possibility of detecting the visual imagination of seven different geometric objects (triangle, circle, square, pentagon, line, hexagon and parallelogram). The power spectral density in the alpha band were compared offline with using common spatial pattern (CSP) and the variance of each channel, obtaining as a best result the calculation of the CSP plus variance in the alpha band and classifying the vector of features with a support vector machine (SVM), obtaining an average result of 52% accuracy and a kappa value of 0.43 in the classification of the seven geometrical shapes, reaching up to 83% and a kappa value of 0.78 for a single user.