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Convolutional Neural Networks (DCNNs) can be a useful tool for detecting the stimulus frequency of the SSVEP signal. The transfer learning approach is used to improve the performance of the PodNet in the presence of a low amount of training data. In this research, two publicly available, 35-subject Benchmark and 70-subject BETA databases are used. In the rendered method, information is transferred from a model trained on the large BETA database to a secondary model which has been designed to identify target stimulus frequency in single-participant of the Benchmark database. The results show that the accuracy (95.00 %) and ITR (143.13 bpm) of the proposed approach in single-participant are significantly higher than the CCA ( p  &lt; 0.05) and the PodNet ( p  &lt; 0.00001). This study illustrates that using the transfer learning approach can improve the performance of the PodNet in the case of limited training data.

Transfer Learning assisted PodNet for Stimulation Frequency Detection in Steady state visually evoked potential-based BCI Spellers