Adaptive boosting of DNN ensembles for brain-computer interface spellers
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CitationGüney, O. B., Koç, E., Aksoy, C., Çatak, Y., Arslan, Ş. S., & Özkan, H. (9-11 June 2021). Adaptive Boosting of DNN Ensembles for Brain-Computer Interface Spellers. In 2021 29th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). https://doi.org/10.1109/SIU53274.2021.9477841
Steady-state visual evoked potentials (SSVEP) are commonly used in brain computer interface (BCI) applications such as spelling systems, due to their advantages over other paradigms. In this study, we develop a method for SSVEP-based BCI speller systems, using a known deep neural network (DNN), which includes transfer and ensemble learning techniques. We test performance of our method on publicly available benchmark and BETA datasets with leave-one-subject-out procedure. Our method consists of two stages. In the first stage, a global DNN is trained using data from all subjects except one subject that is excluded for testing. In the second stage, the global model is fine-tuned to each subject whose data are used in the training. Combining the responses of trained DNNs with different weights for each test subject, rather than an equal weight, provide better performance as brain signals may differ significantly between individuals. To this end, weights of DNNs are learnt with SAMME algorithm with using data belonging to the test subject. Our method significantly outperforms canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods.