(or check DOI link for updated information) Interactions between Pre-Processing and Classification Methods for Event-Related-Potential Classification: Best-Practice Guidelines for Brain-Computer Interfacing

Abstract Detecting event related potentials (ERPs) from single trials is crit-ical to the operation of many stimulus-driven brain computer interface (BCI) systems. The low strength of the ERP signal compared to the noise (due to artifacts and BCI irrelevant brain processes) makes this a challenging signal detection problem. Previous work has tended to focus on how best to detect a single ERP type (such as the visual oddball response). However, the un-derlying ERP detection problem is essentially the same regardless of stimulus modality (e.g. visual or tactile), ERP component (e.g. P300 oddball response, or the error-potential), measurement system or electrode layout. To investi-gate whether a single ERP detection method might work for a wider range of ERP BCIs we compare detection performance over a large corpus of more than 50 ERP BCI datasets whilst systematically varying the electrode mon-tage, spectral filter, spatial filter and classifier training methods. We identify an interesting interaction between spatial whitening and regularised classifica-tion which made detection performance independent of the choice of spectral filter low-pass frequency. Our results show that pipeline consisting of spectral filtering, spatial whitening, and regularised classification gives near maximal performance in all cases. Importantly, this pipeline is simple to implement and completely automatic with no expert feature selection or parameter tuning re-quired. Thus, we recommend this combination as a “best-practice ” method for ERP detection problems