Transfer Learning assisted PodNet for Stimulation Frequency Detection in Steady state visually evoked potential-based BCI Spellers. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Rostami, Elham; Ghassemi, Farnaz; Tabanfar, Zahra
• Is Part Of: Brain-computer interfaces. Volume 10:Issue 1(2023)
• Page Start: 38
• Page End: 49
• Publication Date: 2023-01-02
• Subjects: Brain-Computer interface speller systems -- deep convolutional neural network -- transfer learning approach -- electroencephalography -- steady state visual evoked potential; Brain-computer interfaces -- Periodicals; Neurology -- Periodicals; 616.800285
• DOI: 10.1080/2326263X.2022.2134623
• Languages: English
• ISSNs: 2326-263X

Abstract

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.