iLab: From Brain to Machine and Back
iossifidis Lab
1.Human muscle fatigue has been studied using a wide variety of exercise models, protocols and assessment methods. Based on the definition of fatigue as ‘any reduction in the maximal capacity to generate force or power output’, the different methods to measure fatigue are discussed. It is argued that reliable and valid measures must include either … Read More “ProgressClub: Muscle Fatigue” »
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” »
Presented on 30.09.2020 by Sebastian Doliwa
Error-related potentials (ErrPs) are the neural signature of error processing. Therefore, the detectionof ErrPs is an intuitive approach to improve the performance of brain-computer interfaces (BCIs). The incorporation of ErrPs in discrete BCIs is well established but the study of asynchronous detection of ErrPs is still in its early stages. Here we show the feasibility … Read More “JournalClub: Online asynchronous decoding of error-related potentials during the continuous control of a robot” »
Presented on 09.09.2020 by Stephan Lehmler
Presented on 26.08.2020 by Felix Grün
Presented on 12.08.2020 by Marie Schmidt
Movement primitives are elementary motion units and can be combined sequentially or simultaneously to compose more complex movement sequences. A movement primitive timeseries consist of a sequence of motion phases. This progression through a set of motion phases can be modeled by Hidden Markov Models (HMMs). HMMs are stochastic processes that model time series data … Read More “JournalClub: Modeling Movement Primitives with Hidden Markov Models for Robotic and Biomedical Applications” »
This work has been conducted in the context of pattern-recognition-based control strategies for electromyographic prostheses. It focuses on the conceptual design, implementation and validation of learning techniques based on the k-nearest neighbour (kNN) scheme for gesture recognition. After theoretical considerations and the identification of the topic within the contexts of prosthetic control, biomedical signals — … Read More “JournalClub: Nearest-Neighbour-Based Learning Techniques for Proportional Myocontrol in Prosthetics” »
Presented on 10.06.2020 by Aline Xavier Fidencio
