Hjorth features and k-nearest neighbors algorithm for visual imagery classification

Abstract Visual imagery is an interesting paradigm for use in Brain-Computer Interface systems. Through visual imagery we can extend the potential of BCI systems beyond motor imagery or evoked potentials. In this work we have studied the possibility of classifying different visual imagery shapes in the time domain using EEG signals, with the Hjorth parameters and k-nearest neighbors classifier 69% accuracy has been obtained with a Cohen's kappa value of 0.64 in the classification of seven geometric shapes, obtaining results superior to other related works.