Combining the Benefits of CCA and SVMs for SSVEP-based BCIs in Real-world Conditions

In this paper we propose a novel method for SSVEP classification that combines the benefits of the inherently multi-channel CCA, the state-of-the-art method for detecting SSVEPs, with the robust SVMs, one of the most popular machine learning algorithms. The employment of SVMs, except for the benefit of robustness, provides us also with a confidence score allowing to dynamically trade-off the trial length with the accuracy of the classifier, and vice versa. By balancing this trade-off we are able to offer personalized self-paced BCIs that maximize the ITR of the system. Furthermore, we propose to perturb the template frequencies of CCA so as to accommodate with real world BCI applications requirements, where the environmental conditions may not be ideal compared to existing methods that rely on the assumption of soundproof and distraction-free environments.