… We summarize deep learning techniques for BCI applications. To our best knowledge, we …
Deep learning models for BCI. • We provide guidelines for choosing a suitable deep learning …

… the newer methods of deep learning. We explore two different types of deep learning methods,
namely… The results prove the superiority of deep learning methods in comparison with the …

… Currently, the brain-computer interface (BCI) systems provide … classifiers, transfer learning
approach, and deep learning, as … Deep learning techniques were developed to achieve the …

Today’s life assistive devices were playing significant role in our life to communicate with
others. In that modality Human Computer Interface (HCI) based Electrooculogram (EOG) …

… Researchers are doing a lot of work on deep learning-… deep learning models for EEG-based
BCI applications. Therefore, we introduce this study to the recent proposed deep learning-…

… By merging different kinds of networks, we can extract deeper features than using the deep
learning algorithm alone [44] (see Appendix C for a more detailed overview of Deep Learning…

… based on deep learning for decoding sensory information from non-invasively recorded
Electroencephalograms (EEG). It can either be used in a passive Brain-Computer Interface (BCI) …

… deep-learning based continuous BCI control. Main results. We report that: (1) deep learning
… detected and used to improve performance through deep learning methods, and (3) tuning …

… We propose to learn a set of high-level feature representations through deep learning
algorithm, referred to as Deep Motor Features (DeepMF), for brain computer interface (BCI) with …

… We propose a unified deep learning framework to bridge BrainComputer Interface and
Internet of Things in order to enable cognitive interactivity. We propose WAS-LSTM to extract inter…