IEEE-Prime: Development of a Scalable Analog Front-End for Brain-Computer Interfaces

Posted on By jannis
scientific publication, teaching

Sebastian Doliwa∗, Andreas Erbsl ̈oh†, Karsten Seidl†‡ and Ioannis Iossifidis∗∗University of Applied Science Ruhr-West, Institute for Computer Science, Mülheim an der Ruhr, Germany†University of Duisburg-Essen, Electronic Components and Circuits, Duisburg, Germany‡Fraunhofer Institute for Microelectronic Circuits and Systems, Duisburg, Germany In the context of the development of an implantable embedded system interfacing brain activity and enabling … Read More “IEEE-Prime: Development of a Scalable Analog Front-End for Brain-Computer Interfaces” »

BioRob2022: Biologically Inspired Model for Timed Motion in Robotic Systems

Posted on By jannis
publication, teaching

Sebastian Doliwa and Muhammad Ayaz Hussain and Tim Sziburis and Ioannis Iossifidis Timing plays a vital role in the generation of naturalistic behavior satisfying all constraints arising from interacting with a dynamic environment while adapting the planning and execution of action sequences on-line.In biological systems, many of the physiological and anatomical functions follow a particular … Read More “BioRob2022: Biologically Inspired Model for Timed Motion in Robotic Systems” »

Pallets Detection and position tracking for automated guided vehicles

Posted on By Muhammad Saif-Ur-Rehman
teaching

Bachelorarbeitsthema Significance of the proposed Master thesis The use of autonomous vehicles is vital in the manufacturing and distribution operations. The automated guided vehicles (AGVs) provide reliable and efficient product handling in several industrial applications. Goals of the proposed Master thesis In this Master thesis, we are aiming a deep learning-based solution for pallet detection … Read More “Pallets Detection and position tracking for automated guided vehicles” »

Towards wearable BCI systems that leverage contextual neuromechanics and edge-computing

Posted on By jannis No Comments on Towards wearable BCI systems that leverage contextual neuromechanics and edge-computing
Grant

Bilateral Cooperation in Computational Neuroscience betweenGermany and USA (under review) Consortium: Prof. Dr. Francisco Valero-Cuevas, University of Southern California (US), Prof. Dr. Christian Klaes, Ruhr University Bochum (GER), Prof. Dr. Karsten Seidl, University of Duisburg-Essen (GER), Prof. Dr. Ioannis Iossifidis, Ruhr West University of Applied Sciences (GER) We proposes a novel brain computer interface (BCI) for humans paralyzed … Read More “Towards wearable BCI systems that leverage contextual neuromechanics and edge-computing” »

JournalClub: Adversarial Domain Adaptation For Stable Brain-Machine Interfaces

Posted on By stephan No Comments on JournalClub: Adversarial Domain Adaptation For Stable Brain-Machine Interfaces
journal club

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” »

JournalClub: Cross-Domain MLP and CNN Transfer Learning for Biological Signal Processing: EEG and EMG

Posted on By stephan
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” »

JournalClub: FS-HGR: Few-Shot Learning for Hand Gesture Recognition via Electromyography

Posted on By stephan No Comments on JournalClub: FS-HGR: Few-Shot Learning for Hand Gesture Recognition via Electromyography
journal club

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” »