Classification of code-modulated visual evoked potentials using adaptive modified covariance beamformer and EEG signals

•A novel c-VEP target detection framework is proposed in this paper.•Different user parameter-free methods are used to estimate robust covariance matrix.•Different adaptive and robust beamformers are proposed to detect the c-VEP targets.•All the proposed methods significantly improve the classification accuracy and ITR.•The proposed methods outperform existing state-of-the-art methods. [Display omitted] Objective: In general, brain computer interface (BCI) studies based on code-modulated Visual Evoked Potentials (c-VEP) use m-sequences to decode EEG responses to visual stimuli. BCI systems based on the c-VEP paradigm can simultaneously present a large number of commands, which results in a significantly high information transfer rate (ITR). Spatiotemporal beamforming (STB) is one of the commonly used approaches in c-VEP-based BCI systems. Approach: In the current work, a novel STB-based technique is proposed to detect the gazed targets. The proposed method improves the performance of conventional STB-based techniques by providing a robust estimation of the covariance matrix in short stimulation times. Different user parameter-free methods, including the convex combination (CC), the general linear combination (GLC), and the modified versions of these techniques, are used to estimate a reliable and robust covariance matrix when a small number of repetitions are available. Main results: The stimulus presentation rate of 120 Hz is used to assess the performance of the proposed structures. Our proposed methods improved the classification accuracy by an average of 20% compared to the conventional STB method at the shortest stimulation time. The proposed method achieves an average ITR of 157.07 bits/min by using only two repetitions of the m-sequences. Significance: The results show that our proposed methods perform significantly better than the conventional STB technique in all stimulation times.