Calibration of brain-machine interfaces exploiting event-related potentials has to be performed for each experimental paradigm. Even if these signals have been used in previous experiments with different protocols. We show that use of signals from previous experiments can reduce the calibration time for single-trial classification of error-related potentials. Compensating latency variations across tasks yield up to a 50% reduction the training period in new experiments without decrease in online performance compared to the standard training.
Olaf Blanke, José del Rocio Millán Ruiz, Ronan Boulic, Bruno Herbelin, Ricardo Andres Chavarriaga Lozano, Fumiaki Iwane
Friedhelm Christoph Hummel, Takuya Morishita, Pierre Theopistos Vassiliadis, Claudia Bigoni, Andéol Geoffroy Cadic-Melchior