title: Quantitative analysis of task selection for brain-computer interfaces.
abstract: OBJECTIVE
  To assess quantitatively the impact of task selection in the performance of brain-computer interfaces (BCI).
  
  
  METHODS
  We consider the task-pairs derived from multi-class BCI imagery movement tasks in three different datasets. We analyze for the first time the benefits of task selection on a large-scale basis (109 users) and evaluate the possibility of transferring task-pair information across days for a given subject.
  
  
  RESULTS
  Selecting the subject-dependent optimal task-pair among three different imagery movement tasks results in approximately 20% potential increase in the number of users that can be expected to control a binary BCI. The improvement is observed with respect to the best task-pair fixed across subjects. The best task-pair selected for each subject individually during a first day of recordings is generally a good task-pair in subsequent days. In general, task learning from the user side has a positive influence in the generalization of the optimal task-pair, but special attention should be given to inexperienced subjects.
  
  
  CONCLUSIONS
  These results add significant evidence to existing literature that advocates task selection as a necessary step towards usable BCIs. This contribution motivates further research focused on deriving adaptive methods for task selection on larger sets of mental tasks in practical online scenarios.
authors: Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, The Netherlands.
authors: Alberto
authors: A
authors: Llera
authors: Vicenç
authors: V
authors: Gómez
authors: Hilbert J
authors: HJ
authors: Kappen
mesh_terms: D062207:Brain-Computer Interfaces; D004562:Electrocardiography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology; D013647:Task Performance and Analysis