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Investigating the addition of singing imagery as a control task in motor imagery BCI

Active BCIs often rely on the performance of various imagery tasks that can be detected and decoded as commands for operating an external device. An increase in the number of these commands would significantly improve the functionality of the system and, as a result, quality of life for the user. This study focuses on augmenting the practicality and performance of such systems by investigating the mental task of singing imagery. Singing imagery is the simple act of imagining singing a song in your head and is a common experience for most people. However, despite its intuitive and straightforward nature, the potential of singing imagery for improving the performance of active BCIs or increasing their number of commands has not been fully investigated.In this study, along with various binary analyses, singing imagery is combined with a set of conventional tasks in BCI design (i.e., the motor imagery of large body parts like hands, feet, and tongue) for 4-class, 5-class (adding a “rest” state), and ultimately 6-class scenarios to evaluate the possibilities of enhancing the active BCI systems. Incorporating a singing imagery task in the 4 and 5-class combinations yielded up to 6.6% and 6.7% improvements in classification accuracy for the two cases, respectively. Furthermore, for the 6-class scenario, accuracies as high as 49.8%, which is well above the chance level of 16.7%, were achieved.The preliminary results, obtained from five participants, suggested that singing imagery could be a viable option for improving the classification accuracy or increasing the number of commands.