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A BCI user awareness of an error is associated with a cortical signaturenamed error-related potential (ErrP). The incorporation of ErrPs' detection inBCIs can improve BCIs' performance. This work is three-folded. First, weinvestigate if an ErrP classifier is transferable from able-bodied participantsto participants with spinal cord injury (SCI). Second, we test this genericErrP classifier with SCI and control participants, in an online experimentwithout offline calibration. Third, we investigate the morphology of ErrPs inboth groups of participants. We used previously recordedelectroencephalographic (EEG) data from able-bodied participants to train anErrP classifier. We tested the classifier asynchronously, in an onlineexperiment with 16 new participants: 8 participants with SCI and 8 able-bodiedcontrol participants. The experiment had no offline calibration andparticipants received feedback regarding the ErrPs' detection from its start.The generic classifier was not trained with the user's brain signals. Still,its performance was optimized during the online experiment with the use ofpersonalized decision thresholds. Participants with SCI presented anon-homogenous ErrP morphology, and four of them did not present clear ErrPsignals. The generic classifier performed above chance level in participantswith clear ErrP signals, independently of the SCI (11 out of 16 participants).Three out of the five participants that obtained chance level results with thegeneric classifier would have not benefited from the use of a personalizedclassifier. This work shows the feasibility of transferring an ErrP classifierfrom able-bodied participants to participants with SCI, for asynchronousdetection of ErrPs in an online experiment without offline calibration, whichprovided immediate feedback to the users.

Online asynchronous detection of error-related potentials in participants with a spinal cord injury using a generic classifier