—Objective: The performance of an EEG-based
brain-computer interface (BCI) system is highly dependent
on signal preprocessing. This manuscript presents a filtering method to improve the feature classification algorithms typically used in BCI. Methods: A graph Laplacian
quadratic form using the Phase Locking Value (PLV) is
applied to generate a new filtered signal in the preprocessing stage. Results: The accuracy of the classification algorithms improved significantly (up to 27.18% in the BCI
Competition IV dataset, and up to 42.56% with records made
with an Emotiv EPOC+). In addition, the proposed filtering
algorithm has similar or better results when compared with
the Filter Bank Common Spatial Pattern (FBCSP), which has
disadvantages in a multiclass classification. Conclusion:
This paper shows how our PLV-based filtering between EEG
channels could improve the performance of a BCI.
