RESUMEN
Stimulus proximity has been shown to have an influence on the classification performance of a steady-state visual evoked potential based brain-computer interface (SSVEP-BCI). Multiple visual stimuli placed close to each other compete for neural representations leading to the effect of competing stimuli. In this study, we propose a convolutional neural network (CNN) based classification method to enhance the detection accuracy of SSVEP in the presence of competing stimuli. A seven-class SSVEP dataset from ten healthy participants was used for evaluating the performance of the proposed method. The results were compared with the classic canonical correlation analysis (CCA) detection algorithm. We investigated whether the CNN parameters learned on one inter-stimulus distance (ISD) can generalize across to other ISDs and sessions. The proposed CNN obtained a significantly higher classification accuracy than CCA in both the offline (75.3% vs. 67.9%, (p <; 10-3)) and the simulated online (71.3% vs. 60.7%, (p <; 10-3)) conditions for the closest ISD. The results suggest the following: the CNN is robust in decoding SSVEP across different ISDs, and can be trained independent of the ISD resulting in a model that generalizes to other ISDs.
