Classifier-based latency estimation: a novel way to estimate and predict BCI accuracy.

OBJECTIVE: Brain-computer interfaces (BCIs) that detect event-related potentials (ERPs) rely on classification schemes that are vulnerable to latency jitter, a phenomenon known to occur with ERPs such as the P300 response. The objective of this work was to investigate the role that latency jitter plays in BCI classification. APPROACH: We developed a novel method, classifier-based latency estimation (CBLE), based on a generalization of Woody filtering. The technique works by presenting the time-shifted data to the classifier, and using the time shift that corresponds to the maximal classifier score. MAIN
RESULTS: The variance of CBLE estimates correlates significantly (p < 10(-42)) with BCI accuracy in the Farwell-Donchin BCI paradigm. Additionally, CBLE predicts same-day accuracy, even from small datasets or datasets that have already been used for classifier training, better than the accuracy on the small dataset (p < 0.05). The technique should be relatively classifier-independent, and the results were confirmed on two linear classifiers. SIGNIFICANCE: The results suggest that latency jitter may be an important cause of poor BCI performance, and methods that correct for latency jitter may improve that performance. CBLE can also be used to decrease the amount of data needed for accuracy estimation, allowing research on effects with shorter timescales.