Adaptive boosting of DNN ensembles for brain-computer interface spellers

Warning The system is temporarily closed to updates for reporting purpose.

Güney, Osman Berke and Koç, Emirhan and Aksoy, Can and Çatak, Yiğit and Arslan, Şuayb and Özkan, Hüseyin (2021) Adaptive boosting of DNN ensembles for brain-computer interface spellers. In: 2021 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey

[img]PDF - Registered users only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader

Official URL: http://dx.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.

Item Type:Papers in Conference Proceedings
Uncontrolled Keywords:steady state visually evoked potentials, brain-computer interfaces, ensemble, deep learning, transfer learning adaptive boosting
ID Code:41908
Deposited By:Hüseyin Özkan
Deposited On:25 Aug 2021 16:24
Last Modified:25 Aug 2021 16:24

Repository Staff Only: item control page