Reducing the training time for the SSVEP-based music player application

Abstract

This work focusses on reducing the training time required for a brain–computer interface (BCI) music player based on steady-state visually evoked potentials (SSVEPs). The music player is menu driven, featuring three different interfaces with up to six continuously flickering stimuli, similar to typical smart phone applications. This work investigates whether it is possible to go from a menu driven training approach to one which uses a single stimuli session only for training, or one which uses solely the data collected from the menu with the largest number of stimuli. Results show that the latter reduces the training time by 38.90%, specifically from 21 to 12.83 min without significant degradation in classification performance. Furthermore, promising results were also revealed when using a subject independent classifier which avoids individual training for new subjects by using training data from a database of other subjects. Although this work was targeted towards the brain controlled music player, the results are applicable to any SSVEP based BCI system having multiple interfaces with different number of flickering stimuli.