Optimization of Single-Trial Detection of Event-Related Potentials Through Artificial Trials

Goal: Many brain-computer interface (BCI) classification techniques rely on a large number of labeled brain responses to create efficient classifiers. A large database representing all of the possible variability in the signal is impossible to obtain in a short period of time, and prolonged calibration times prevent efficient BC! use. We propose to improve BCIs based on the detection of event-related potentials (ERPs) in two ways. Methods: First, we increase the size of the training database by considering additional deformed trials. The creation of the additional deformed trials is based on the addition of Gaussian noise, and on the variability of the ERP latencies. Second, we exploit the variability of the ERP latencies by combining decisions across multiple deformed trials. These new methods are evaluated on data from 16 healthy subjects participating in a rapid serial visual presentation task. Results: The results show a significant increase in the performance of single-trial detection with the addition of artificial trials, and the combination of decisions obtained from altered trials. When the number of trials to train a classifier is low, the proposed approach allows us improve performance from an AUC of 0.533 ± 0.080 to 0.905 ± 0.053. This improvement represents approximately an 80% reduction in classification error. Conclusion: These results demonstrate that artificially increasing the training dataset leads to improved single-trial detection. Significance: Calibration sessions can be shortened for BCIs based on ERP detection.