You are wrong!: automatic detection of interaction errors from brain waves
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Comparison of classification methods for P300 brain-computer interface on disabled subjects
Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
Investigating the use of brain-computer interaction to facilitate creativity
AH '12 Proceedings of the 3rd Augmented Human International Conference
Detecting error-related negativity for interaction design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A predictive speller controlled by a brain-computer interface based on motor imagery
ACM Transactions on Computer-Human Interaction (TOCHI)
Objective and subjective evaluation of online error correction during P300-based spelling
Advances in Human-Computer Interaction - Special issue on Using Brain Waves to Control Computers and Machines
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Error potentials (ErrPs), that is, alterations of the EEG traces related to the subject perception of erroneous responses, have been suggested to be an elegant way to recognize misinterpreted commands in brain-computer interface (BCI) systems. We implemented a P300-based BCI speller that uses a genetic algorithm (GA) to detect P300s, and added an automatic error-correction system (ECS) based on the single-sweep detection of ErrPs. The developed system was tested on-line on three subjects and here we report preliminary results. In two out of three subjects, the GA provided a good performance in detecting P300 (90% and 60% accuracy with 5 repetitions), and it was possible to detect ErrP with an accuracy (roughly 60%) well above the chance level. In our knowledge, this is the first time that ErrP detection is performed on-line in a P300-based BCI. Preliminary results are encouraging, but further refinements are needed to improve performances.