Error-related potentials (ErrPs) are the neural signature of error processing. Therefore, the detection of ErrPs is an intuitive approach to improve the performance of brain-computer interfaces (BCIs). The incorporation of ErrPs in discrete BCIs is well established but the study of asynchronous detection of ErrPs is still in its early stages. Here we show the feasibility of asynchronously decoding ErrPs in an online scenario. For that, we measured EEG in 15 participants while they controlled a robotic arm towards a target using their right hand. In 30% of the trials, the control of the robotic arm was halted at an unexpected moment (error onset) in order to trigger error-related potentials. When an ErrP was detected after the error onset, participants regained the control of the robot and could finish the trial. Regarding the asynchronous classification in the online scenario, we obtained an average true positive rate (TPR) of 70% and an average true negative rate (TNR) of 86.8%. These results indicate that the online asynchronous decoding of ErrPs was, on average, reliable, showing the feasibility of the asynchronous decoding of ErrPs in an online scenario.

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The time-locked classification results suggest that the masked and unmasked errors were indistinguishable in terms of classification. The asynchronous classification results suggest that the feedback modality did not hinder the asynchronous detection of ErrPs.

For brain–computer interface (BCI) users, the awareness of an error is associated with a cortical signature known as an error-related potential (ErrP). The incorporation of ErrP detection into BCIs can improve their performance. Objective. This work has three main aims. First, we investigate whether an ErrP classifier is transferable from able-bodied participants to participants with a spinal cord injury (SCI). Second, we test this generic ErrP classifier with SCI and control participants, in an online experiment without offline calibration. Third, we investigate the morphology of ErrPs in both groups of participants. Approach. We used previously recorded electroencephalographic data from able-bodied participants to train an ErrP classifier. We tested the classifier asynchronously, in an online experiment with 16 new participants: 8 participants with SCI and 8 able-bodied control participants. The experiment had no offline calibration and participants received feedback regarding the ErrP detections from the start. To increase the fluidity of the experiment, feedback regarding false positive ErrP detections was not presented to the participants, but these detections were taken into account in the evaluation of the classifier. The generic classifier was not trained with the user’s brain signals. However, its performance was optimized during the online experiment by the use of personalized decision thresholds. The classifier’s performance was evaluated using trial-based metrics, which considered the asynchronous detection of ErrPs during the entire trial’s duration. Main results. Participants with SCI presented a non-homogenous ErrP morphology, and four of them did not present clear ErrP signals. The generic classifier performed better than chance in participants with clear ErrP signals, independently of the SCI (11 out of 16 participants). Three out of the five participants that obtained chance level results with the generic classifier would have not benefitted from the use of a personalized classifier. Significance. This work shows the feasibility of transferring an ErrP classifier from able-bodied participants to participants with SCI, for asynchronous detection of ErrPs in an online experiment without offline calibration, which provided immediate feedback to the users.

CYBATHLON is an international championship where people with severe physical disabilities compete with the aid of state-of-the-art assistive technology. In one of the disciplines, the BCI Race, tetraplegic pilots compete in a computer game race by controlling an avatar with a brain-computer interface (BCI). This competition offers a perfect opportunity for BCI researchers to study long-term training effects in potential end-users, and to evaluate BCI performance in a realistic environment. In this work, we describe the BCI system designed by the team Mirage91 for participation in the CYBATHLON BCI Series 2019, as well as in the CYBATHLON 2020 Global Edition. Furthermore, we present the BCI’s interface with the game and the main methodological strategies, along with a detailed evaluation of its performance over the course of the training period, which lasted 14 months. The developed system was a 4-class BCI relying on task-specific modulations of brain rhythms. We implemented inter-session transfer learning to reduce calibration time, and to reinforce the stability of the brain patterns. Additionally, in order to compensate for potential intra-session shifts in the features’ distribution, normalization parameters were continuously adapted in an unsupervised fashion. Across the aforementioned 14 months, we recorded 26 game-based training sessions. Between the first eight sessions, and the final eight sessions leading up to the CYBATHLON 2020 Global Edition, the runtimes significantly improved from 255 ± 23 s (mean ± std) to 225 ± 22 s, respectively. Moreover, we observed a significant increase in the classifier’s accuracy from 46 to 53%, driven by more distinguishable brain patterns. Compared to conventional single session, non-adaptive BCIs, the inter-session transfer learning and unsupervised intra-session adaptation techniques significantly improved the performance. This long-term study demonstrates that regular training helped the pilot to significantly increase the distance between task-specific patterns, which resulted in an improvement of performance, both with respect to class separability in the calibration data, and with respect to the game. Furthermore, it shows that our methodological approaches were beneficial in transferring the performance across sessions, and most importantly to the CYBATHLON competitions.