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” »
Category: journal club
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” »
Gulletta, Gianpaolo, Eliana Costa e Silva, Wolfram Erlhagen, Ruud Meulenbroek, Maria Fernanda Pires Costa, and Estela Bicho. “A Human-like Upper-Limb Motion Planner: Generating Naturalistic Movements for Humanoid Robots.” International Journal of Advanced Robotic Systems, (March 2021). https://doi.org/10.1177/1729881421998585. As robots are starting to become part of our daily lives, they must be able to cooperate in … Read More “JournalClub: A Human-like Upper-limb Motion Planner: Generating naturalistic movements for humanoid robots” »
Gulletta, Gianpaolo, et al. “Human-Like Arm Motion Generation: A Review.” Robotics, vol. 9, no. 4, Dec. 2020, p. 102, doi:10.3390/robotics9040102. In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most … Read More “JournalClub: Human-Like Arm Motion Generation: A Review” »
In my current project I encountered a stumbling block: inefficient/insufficient exploration. While not really useful as a solution to my problem I finally read this paper I had wanted to for a while. Here is the abstract: Yuri Burda*, Harrison Edwards*, Oleg Klimov (all OpenAI), Amos Storkey (Univ. of Edinburgh) *main authors We introduce an … Read More “JournalClub: Exploration by Random Network Distillation” »
Validating appropriateness and naturalness of human-robot interaction (HRI) is commonly performed by taking subjective measures from human interaction partners, e.g. questionnaire ratings. Although these measures can be of high value for robot designers, they are very sensitive and can be inaccurate and/or biased. In this paper we propose and validate a neuro-based method for objectively … Read More “JournalClub: A neuro-based method for detecting context-dependent erroneous robot action” »
Brain–machine interfaces (BMIs) are promising devices that can be used as neuroprostheses by severely disabled individuals. Brain surface electroencephalograms (electrocorticograms, ECoGs) can provide input signals that can then be decoded to enable communication with others and to control intelligent prostheses and home electronics. However, conventional systems use wired ECoG recordings. Therefore, the development of wireless … Read More “JournalClub: A Fully Implantable Wireless ECoG 128Channel Recording Device for Human Brain–Machine Interfaces” »
JournalClub: Navigating features: a topologically informed chart of electromyographic features space
The success of biological signal pattern recognition depends crucially on the selection of relevant features. Across signal and imaging modalities, a large number of features have been proposed, leading to feature redundancy and the need for optimal feature set identification. A further complication is that, due to the inherent biological variability, even the same classification … Read More “JournalClub: Navigating features: a topologically informed chart of electromyographic features space” »
A salient feature of human motor skill learning is the ability to exploit similarities across related tasks. In biological motor control, it has been hypothesized that muscle synergies, coherent activations of groups of muscles, allow for exploiting shared knowledge. Recent studies have shown that a rich set of complex motor skills can be generated by … Read More “JournalClub: Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems” »