Two Seconds to Speak: Increasing Communication Speed for fMRI-Based Brain-Computer Interfaces.

Brain-computer interfaces (BCIs) can provide alternative, motor-independent means of communication for people who have lost motor function. A promising variant is the functional magnetic resonance imaging (fMRI)-based BCI, which exploits information on hemodynamic brain activity evoked by performing different mental tasks. However, due to the sluggish nature of the hemodynamic response, a current challenge is to make these BCIs as efficient and fast as possible to allow useful clinical application. Furthermore, there is yet no consensus on optimal mental-task selection for multi-voxel pattern analysis-based decoding, nor whether certain tasks generalize well across users, or if individualized task selection would yield a higher decoding accuracy. To increase BCI efficiency, we tested whether distributed patterns of 3T-fMRI brain activation evoked by two-second mental tasks could be reliably discriminated in 2- to 7-class classification. In addition, we identified optimal mental-task combinations for high-accuracy classification across all classes. Finally, we examined whether individualized task selection-based on subjects' previous decoding performance ( tasks) or their subjective preference (-was superior to the other in a yes/no communication paradigm. The 2-class decoding resulted in a mean accuracy of 78% and 3- to 7-class accuracies were above chance level. Mental calculation and spatial navigation were most frequently associated with the highest decoding accuracy. Furthermore, subjects could encode yes/no answers using their and tasks with mean accuracies of 83% and 81%, respectively. This implies that this paradigm, using short encoding durations, is well-suited to the diversity of patients and could greatly increase BCI efficiency. Impact Statement This study advances functional magnetic resonance imaging (fMRI)-based brain-computer interfaces (BCIs) by showing that brain activation evoked by two-second mental tasks can be reliably decoded with multi-voxel pattern analysis, significantly improving fMRI-BCI efficiency while still achieving high accuracy. By exploring the differentiability of seven different mental tasks, using binary and multiclass classification of up to seven classes, and individualized task selection, we provide insights into optimizing mental-task paradigms for patient-tailored fMRI-BCIs. Given the variability of cognitive abilities in motor-impaired individuals, patient-tailored BCIs with a diverse range of mental tasks are highly welcome. These findings contribute to faster, more intuitive, and less cognitively demanding hemodynamic BCIs.