The P300 as a marker of waning attention and error propensity
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Editorial: Recent advances in brain-machine interfaces
Neural Networks
Towards ambulatory brain-computer interfaces: a pilot study with P300 signals
Proceedings of the International Conference on Advances in Computer Enterntainment Technology
Utilizing fuzzy-SVM and a subject database to reduce the calibration time of P300-based BCI
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Subliminal cues while teaching: HCI technique for enhanced learning
Advances in Human-Computer Interaction - Special issue on subliminal communication in human-computer interaction
Brain-computer interface: generic control interface for social interaction applications
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
UAHCI'11 Proceedings of the 6th international conference on Universal access in human-computer interaction: users diversity - Volume Part II
Brain computer interface control via functional connectivity dynamics
Pattern Recognition
Brain-Computer interface games: towards a framework
ICEC'12 Proceedings of the 11th international conference on Entertainment Computing
BCI could make old two-player games even more fun: a proof of concept with "connect four"
Advances in Human-Computer Interaction - Special issue on Using Brain Waves to Control Computers and Machines
Artificial Intelligence in Medicine
International Journal of Technology Enhanced Learning
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We present a Brain-Computer Interface (BCI) game, the MindGame, based on the P300 event-related potential. In the MindGame interface P300 events are translated into movements of a character on a three-dimensional game board. A linear feature selection and classification scheme is applied to identify P300 events and calculate gradual feedback features from a scalp electrode array. The classification during the online run of the game is computed on a single-trial basis without averaging over subtrials. We achieve classification rates of 0.65 on single-trials during the online operation of the system while providing gradual feedback to the player.