Influence of the number of channels and classification algorithm on the performance robustness to electrode shift in low-frequency steady-state visual evoked potential-based brain–computer interfaces

Elevating the classification accuracy and information transfer rate of brain–computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) remains an active investigation. However, it has often been ignored that the performance of SSVEP-based BCIs can be affected through minor displacement of the electrodes from their optimal locations in practical applications because of the mislocation of electrodes and/or concurrent use of electroencephalography (EEG) devices with external devices, such as virtual reality headsets. In this study, we evaluated the performance robustness of low-frequency SSVEP-based BCIs with respect to the changes in electrode locations for various channel configurations and classification algorithms. Our experiments had 21 participants, where EEG signals were recorded from the scalp electrodes densely attached to the participants’ occipital area. The classification accuracies for all the possible cases of electrode location shifts for various channel configurations (1–3 channels) were calculated using five training-free SSVEP classification algorithms, i.e., the canonical correlation analysis (CCA), extended CCA, filter bank CCA, multivariate synchronization index (MSI), and extended MSI (EMSI). Then, the performances of the BCIs were evaluated using two measures, i.e., the average classification accuracy (ACA) across the electrode shifts and robustness to the electrode shift (RES). Our results showed that the ACA increased with an increase in the number of channels regardless of the algorithm. However, the RES was enhanced with an increase in the number of channels only when MSI and EMSI were employed. While both ACA and RES values for the five algorithms were similar under the single-channel condition, both ACA and RES values for MSI and EMSI were higher than those of the other algorithms under the multichannel (i.e., two or three electrodes) conditions. In addition, comparing the ACA and RES values under the multichannel conditions, EMSI outperformed MSI. In conclusion, our results suggest that the use of multichannel configuration and employment of EMSI could make the performance of low-frequency SSVEP-based BCIs more robust to the electrode shift from the optimal locations.