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Individuals with severe motor impairment can use event-related desynchronization (ERD) based BCIs as assistive technology. Auto-calibrating and adaptive ERD-based BCIs that users control with motor imagery tasks (“ SMR-AdBCI ”) have proven effective for healthy users. We aim to find an improved configuration of such an adaptive ERD-based BCI for individuals with severe motor impairment as a result of spinal cord injury (SCI) or stroke. We hypothesized that an adaptive ERD-based BCI, that automatically selects a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration ( “Auto-AdBCI” ) could allow for higher control performance than a conventional  SMR-AdBCI . To answer this question we performed offline analyses on two sessions (21 data sets total) of cue-guided, five-class electroencephalography (EEG) data recorded from individuals with SCI or stroke. On data from the twelve individuals in Session 1, we first identified three bipolar derivations for the  SMR-AdBCI . In a similar way, we determined three bipolar derivations and four mental tasks for the  Auto-AdBCI . We then simulated both, the  SMR-AdBCI  and the  Auto-AdBCI  configuration on the unseen data from the nine participants in Session 2 and compared the results. On the unseen data of Session 2 from individuals with SCI or stroke, we found that automatically selecting a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration ( Auto-AdBCI ) significantly ( p  < 0.01) improved classification performance compared to an adaptive ERD-based BCI that only used motor imagery tasks ( SMR-AdBCI ; average accuracy of 75.7 vs. 66.3%).

Non motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment