iLab: From Brain to Machine and Back

Farshchian, A., Gallego, J. A., Cohen, J. P., Bengio, Y., Miller, L. E., & Solla, S. A. (2018). Adversarial domain adaptation for stable brain-machine interfaces. arXiv preprint arXiv:1810.00045. Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option to restore voluntary movements after paralysis. These devices are based on the ability to extract information about … Read More “JournalClub: Adversarial Domain Adaptation For Stable Brain-Machine Interfaces” »

J. J. Bird, J. Kobylarz, D. R. Faria, A. Ekárt and E. P. Ribeiro, “Cross-Domain MLP and CNN Transfer Learning for Biological Signal Processing: EEG and EMG,” in IEEE Access, vol. 8, pp. 54789-54801, 2020, doi: 10.1109/ACCESS.2020.2979074. Abstract: In this work, we show the success of unsupervised transfer learning between Electroencephalographic (brainwave) classification and Electromyographic (muscular … Read More “JournalClub: Cross-Domain MLP and CNN Transfer Learning for Biological Signal Processing: EEG and EMG” »

E. Rahimian, S. Zabihi, A. Asif, D. Farina, S. F. Atashzar and A. Mohammadi, “FS-HGR: Few-Shot Learning for Hand Gesture Recognition via Electromyography,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1004-1015, 2021, doi: 10.1109/TNSRE.2021.3077413. Abstract: This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread … Read More “JournalClub: FS-HGR: Few-Shot Learning for Hand Gesture Recognition via Electromyography” »