Cluster analysis of CCA coefficients for robust detection of the asynchronous SSVEPs in brain–computer interfaces

•We propose a new method of SSVEP detection in multichannel, asynchronous BCIs.•It is compared with three competitive spatial filtering and detection techniques.•The methods are evaluated with the use of EEG signals measured for 21 subjects.•The results show significant improvement of the ITR for a large group of users.•Only a short calibration session is required to operate the interface.

In this paper, a method for detection of steady-state visual evoked potentials in a non-invasive, multiple channel, asynchronous brain–computer interface is proposed. It is based on the canonical correlation analysis spatial filter for identifying optimal weighted combinations of electrode signals, followed by a cluster analysis of its coefficients for a fast and accurate SSVEP detection. High information transfer rates can be achieved after a short calibration session. The proposed algorithm, a standard spectrum analysis approach, and two competitive spatial filtering and detection methods were evaluated in a series of experiments with the use of data from 21 subjects. The obtained results showed a significant improvement in classification accuracy and in an average detection time for a large group of users.