Using Robust Principal Component Analysis to Reduce EEG Intra-Trial Variability.

Practical brain-computer interfaces need to overcome several challenges, including tolerance to signal variability within- and across sessions. We introduce Robust Principal Component Analysis (RPCA) as a potential approach to tackle intra-trial variability. Assuming that subjects undergo the same cognitive process or perform the same task in a short period (e.g., a few seconds), as a result, the signal of interest should be represented by only a few components. We verified this approach on a workload detection task, where subjects needed to pilot a simulated drone. We used RPCA as a processing step to decrease trial variability and assessed its impact on classification accuracy. Our results showed that RPCA significantly increased performance in both at group and subject level analysis. On average, class-balanced accuracy when simulating RPCA online increased from 63.9% up to 70.6% $(p~ 0 . 001)$.