Automatic detection of code-modulated visual evoked potentials using novel covariance estimators and short-time EEG signals

Abstract

Background and Objective:Over the last years, code-modulated visual evoked potentials (cVEP)-based brain–computer interfaces (BCIs) have been developed as robust and non-invasive tools to construct high-speed communication systems. Recently, beamforming-based algorithms have extensively been used in cVEP-based BCI systems because of the need for short-time stimulation and less training data. One of the main drawbacks of the beamforming-based approaches is that their performance highly depends on estimating data covariance matrix and calculating activation patterns. Methods:In the present study, two novel covariance estimators (i.e., the modified convex combination (MCC) and the maximum likelihood (ML) techniques) are proposed to estimate a robust and more reliable covariance matrix. In the ML method, a new sparsity constraint is considered to express the specific eigendecomposition of the covariance matrix as a sparse matrix transform (SMT). Then, the SMT is calculated using the product of pairwise coordinate rotations. These rotations can be constructed by a cross-validation method. Two stimulation presentation rates of 60 and 120 Hz are used for the coding sequence. Results:Both of the suggested approaches (i.e., the MCC and SMT-based techniques) can efficiently improve the performance of the conventional spatiotemporal beamforming-based methods by providing a robust estimate of the covariance matrix in short stimulation times. Based on the experimental results, it can be concluded that the proposed SMT and MCC methods achieve the best results for the 60 and 120 Hz stimulus presentation rates, respectively. However, for both stimulus presentation rates, the proposed SMT and MCC-based methods remarkably outperform other state-of-the-art methods in cVEP-based BCI, such as conventional spatiotemporal beamforming and optimized support vector machines (SVM). Also, the results showed that the 120 Hz stimulus presentation rate provided faster communication. This procedure is performed by obtaining a maximal Information Transfer Rate (ITR) of 187.38 bits/minute. Conclusion:Finally, the present study suggested that the proposed MCC and SMT-based techniques could automatically detect the gazed targets. Also, these methods could be used as non-invasive alternatives over conventional methods.