Regularised task‐related component analysis for robust SSVEP‐based brain‐computer interface

Abstract Recent advancements in steady‐state visual evoked potential‐based brain‐computer interface have been made possible by the widespread use of various spatial filtering methods. Task‐related component analysis has superiority over existing subject‐specific target frequency recognition methods. However, the optimised spatial filters of task‐related component analysis generated from a small training dataset are susceptible to artefacts and noise, which can be overfitted, particularly in short time windows. To tackle this issue, the authors propose a regularised task‐related component analysis that adopts three regularisation approaches to the objective function of task‐related component analysis. Conventionally, the regularisation method is a simple and efficient way to overcome the overfitting problem, especially for a small training dataset. To this end, the proposed regularised task‐related component analyses outperform the conventional task‐related component analysis in terms of average classification accuracy and information transfer rate.