iossifidis Lab
Master Student project at Ruhr West University of Applied Sciences. Students Ivan Cedric Mabou and Franck Wafo Supervision M. Sc. Stephan Lehmler Project site https://gitlab.hs-ruhrwest.de/ This study focused on the application of Transfer Learning (TL) to Sentiment Analysis (SA) using pre-trained word embeddings, as well as the transfer of different neural layers. We investigated and … Read More “Master Project: Transfer Learning for Sentiment Analysis using CNN” »
The Workshop would consist of short presentations (10-20 minutes) on related topics followed by group discussion. In preparation for that we should all prepare by researching some part(s) of that topics listed below: time domain features frequency domain features statistical features dimensionality reduction / feature learning feature selection methods (moments, cumulants, zero crossings, …) hardware … Read More “iLab Workshop on Feature Selection, Analysis and Classification (May, 2, 2022)” »
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 (accepted)” »
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 (submitted)” »
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” »
Student project within the module “Autonomous Systems” at Ruhr West University of Applied Sciences. Students Tim Büttel, Jonas Peltzer, Pierre Pospiech, Jannik Schremmern Supervision Tim Sziburis, Susanne Blex, Felix Grün, Prof. Dr. Ioannis Iossifidis Project site https://gitlab.hs-ruhrwest.de/iSystemsStudentProjects/2020/eeg-emg-gamecontroller
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” »
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” »
