Abstract
The underlying neural cognitive basis of brain-computer interfaces (BCIs) can be revealed by eventrelated potentials (ERPs) analysis, which provides another way of perceiving the extraordinary advancements in the BCI field. However, the stimulus onset asynchronies in BCIs are far shorter than those in traditional ERP studies. Moreover, strong periodic disturbances can be observed in averaged BCI signals; these cause difficulties in evaluating ERP component parameters such as amplitudes and latencies, which are required for ERP analysis. The mechanisms of such disturbances, as well as the methods to suppress them, are still unknown in the literature. In this study, an ERP signal isolation model of a BCI was built, which demonstrated that the overlapping effect of ERP components contributed to the periodic disturbances. To isolate ERP components from the contaminated signals, a Toeplitz method and difference-wave method were further proposed for the solution of the model. Experimental results showed that, by using the Toeplitz method, ERP components could be recovered from the averaged signal corresponding to the non-targets of the BCI; meanwhile, the difference wave exhibited a strong ability to suppress the overlapping effect, and ERP signals with relatively flat baselines could be obtained. These results validated the rationality of the proposed model. The study provides grounds for the explanation and suppression of the periodic disturbances in ERP analyses, while providing reasonable support for cognitive research of BCI.
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