Investigating the Influence of Background Music on the Performance of a cVEP-Based BCI

Brain-computer interfaces (BCIs) like e.g. different types of EEG-based BCI spellers (up to date the most common BCI applications) allow new methods of control and interaction with computers and machines. The impact of background music and noise on user's performance (information transfer rate - ITR and accuracy) has already been investigated for different types of spellers, and usually led to a distractive, decreasing effect on the BCI performance, though there is always room for improving the subjective user experience. In this study, 11 participants used a cVEP-based BCI to perform two spelling tasks “BCI_AND_MUSIC” and “CONTRARY”, while listening via headphones to a standard instrumental song, a self-chosen instrumental song (“own music”), or to noise conditions: “no music” and “white noise”. Objective factors such as the blood pressure and heart rate were measured after each spelling task, and questionnaires were answered. The BCI accuracy was close to 100% in most cases, while the lowest accuracy was reached while listening to the individually selected, self-chosen music (“own music”). Similarly, the ITR was the highest in case of “no music” and lowest in case of “own music” (which, on the other hand, resulted in a better mood and higher excitement of the participants). Thus, while the general condition of “no music” could be recommended for further BCI experiments, additional research regarding individual factors like musicality or listening habits of BCI participants should be considered.

I. Introduction

The fast developing field of brain-computer interfaces (BCIs) has the potential to completely change how people inter-act with computers and other devices [1], [2]. Instead of steady-state visual evoked potentials (SSVEPs), which uses flickering stimuli at specific frequencies [3], a different type of BCI based on code-modulated visual evoked potentials, known as cVEP BCI or c-VEP BCI, was implemented for this research, enabling users to select letters [4]. cVEP BCI utilizes pseudorandom sequences to generate visual stimuli. Compared to fVEP (frequency-modulated VEP) and tVEP (time-modulated VEP), cVEP showed higher accuracies and communication speeds [5].