iLab: From Brain to Machine and Back
iossifidis Lab
Marie Dominique Schmidt1 , Ioannis Iossifidis1 Institute of Computer Science, University of Applied Science Ruhr West, Duisburger Str. 100, 45479 Mülheim an der Ruhr, Germany The upper limbs enable us to perform a variety of tasks in everyday life that require strength and a wide range of motion as well as precision. For coordinated motion, … Read More “Bernstein Conference 2022: Linking muscle activity and motion trajectory” »
Felix Grün1, 2 , Muhammed Saif-ur-Rehman1 , Ioannis Iossifidis1 Department for Computer Science, Ruhr-West University of Applied Sciences, Lützowstraße 5, 46236 Bottrop, Germany Institut für Neuroinformatik, Ruhr-University Bochum, Universitätsstraße 150, 44801 Bochum, Germany The relation between the activity of dopaminergic neurons and the temporal difference error in Reinforcement Learning (RL) problems [1] is well-known to … Read More “Bernstein Conference 2022: Exploring Distribution Parameterizations for Distributional Continuous Control” »
Stephan Johann Lehmler, Muhammad Saif-Ur-Rehman, Ioannis Iossifidis Computer Science, Ruhr West University of Applied Science, 45407 Mülheim an der Ruhr, Germany Institute for Neural Computation, Ruhr-University Bochum, 44801 Bochum, Germany Bioelectrical signals gathered via surface electromyography (sEMG) are the basis of muscle-machine-interfaces (MMI), which makes accurate decoding of those signals an important step in aplications … Read More “Bernstein Conference 2022: Modeling subject specfic surface EMG features by means of deep learning” »
Susanne Blex, Tim Sziburis, Ioannis Iossifidis Department of Computer Science, Ruhr West University of Applied Sciences, 45407 Mülheim an der Ruhr, Germany Institute of Neuroinformatics, Ruhr University Bochum, 44801 Bochum, Germany In our research, we model human upper-limb motion by means of the attractor dynamics approach as a promising candidate for the generation of human-like … Read More “BernsteinConference 2022:A dataset of 3D hand transport trajectories determined by inertial measurements from a single sensor” »
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” »
Preprint https://arxiv.org/abs/2011.14694 AbstractObjective. Brain-computer interfaces (BCIs) enable direct communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) is one of the most common sources of neural signals because of its inexpensive and non-invasivenature. However, interpretation of EEG signals is non-trivial because EEG signals have a low spatial resolution and are … Read More “Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation” »
Journal of Neural Engineering: https://iopscience.iop.org/article/10.1088/1741-2552/ab1e63 In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does … Read More “SpikeDeeptector: a deep-learning based method for detection of neural spiking activity” »
Journal of Neural Engineering: https://iopscience.iop.org/article/10.1088/1741-2552/ab1e63 In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does … Read More “SpikeDeeptector: a deep-learning based method for detection of neural spiking activity” »
State-of-the-art microelectrode array technology enables simultaneous, large-scale single unit recordings from hundreds of channels. Identification of channels recording neural data as compared to noise is the first step for all further analyses. Automatizing this process aims at minimizing the human involvement and time for manual curation. In our previous study, we introduced the “SpikeDeeptector” (SD), … Read More “SfN: Universal Spikedeeptector” »
State-of-the-art microelectrode array technology enables simultaneous, large-scale single unit recordings from hundreds of channels. Identification of channels recording neural data as compared to noise is the first step for all further analyses. Automatizing this process aims at minimizing the human involvement and time for manual curation. In our previous study, we introduced the “SpikeDeeptector” (SD), … Read More “SfN 2019: Universal Spikedeeptector” »
