Approach: In this work, we introduce a single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that is equally useful for EEG-HMI and EMG-HMI paradigms.
In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on a new subjects data, and compare their performance to subject-specific models.
GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier.
no code implementations • 23 Dec 2019 • Muhammad Saif-ur-Rehman, Omair Ali, Robin Lienkaemper, Sussane Dyck, Marita Metzler, Yaroslav Parpaley, Joerg Wellmer, Charles Liu, Brian Lee, Spencer Kellis, Richard Andersen, Ioannis Iossifidis, Tobias Glasmachers, Christian Klaes
We proposed a novel spike sorting pipeline, based on a set of supervised and unsupervised learning algorithms.