Time-frequency analysis of EEG signals from healthy subjects allocated by gender for a subject-independent BCI-based on motor imagery

Most of the recent brain-computer interfaces (BCI) based on motor imagery are designed by taking into account a single user. A BCI system designed for multiple users without the need of extensive training sessions could be a viable solution for the system´s implementation outside research centers. The present work explores the design of an independent-subject BCI. In order to accomplish this, Linear Discriminant Analysis classifiers were designed with the data of a sample of 30 healthy volunteers as a whole group and separated by gender. Three different methods were employed to compute power spectrum features from the volunteer´s electroencephalographic recordings. The results show that it is possible to design an independent-subject BCI for the classification of right or left hand motor imagery with respect of a reference interval with classification accuracies above 70%. The female gender could benefit more from a subject-independent classifier, than the male gender.