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

ABSTRACT

  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 < 0.05) and the PodNet (p < 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.

KEYWORDS:

• Brain-Computer interface speller systems
• deep convolutional neural network
• transfer learning approach
• electroencephalography
• steady state visual evoked potential

Acknowledgments

The authors Acknowledge from The Benchmark database procurers as well as the BETA database producers (Bingchuan Liu et al.) who provided it to our group.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Notes

1. Neuroscan, Inc.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.