Institute of Neuroinformatics

Open Opportunities

Learn to navigate within scenes by leveraging object representation and interaction. ETH Zurich Institute of Neuroinformatics Other organizations: ETH Competence Center - ETH AI Center Biological intelligence excels in adapting to new tasks by reusing prior knowledge, as seen in landmark-based navigation where animals plan routes using spatial relationships. In contrast, machine learning struggles with navigation due to challenges like understanding the effects of actions on perception (equivariance), estimating distances to objects without supervision (localization), and dealing with occlusion. The proposed method addresses these challenges by learning equivariant object representations and composing them into a coherent map, enabling accurate pose estimation and navigation. Successfully tested in a 3D simulation, the goal is to apply this approach to more realistic environments, such as drone navigation, and to demonstrate its utility in various tasks through reinforcement learning. Artificial Intelligence and Signal and Image Processing Course Project

Mental Workload Detection and Classification Using fNIRS and Machine Learning University of Zurich Institute of Neuroinformatics Background Mental Workload: Mental workload, also known as cognitive workload, refers to the mental effort and resources required to perform a specific task or activity. It encompasses various cognitive processes such as attention, memory, problem-solving, decision-making, and perception. The more demanding a task, the higher the mental workload required to perform it effectively. In the context of Human-Computer Interaction (HCI), mental workload is critical as it affects the user's ability to process information efficiently. When the workload exceeds a user's cognitive capacity, information processing slows down, leading to increased errors and reduced performance. Understanding and measuring mental workload is crucial because it affects our productivity, mental health, and overall well-being. Background Stroke: Stroke is a major cause of motor impairments, leading to an increased demand for effective rehabilitation methods. Technological solutions like VR-based rehabilitation and robotic movement training systems show promise in minimally supervised settings. However, maintaining patient engagement is a challenge. Balancing visual, memory, and attentional load is critical, especially for stroke patients sensitive to excessive task load. This project aims to develop an adaptive neurorehabilitation system using fNIRS to measure and adjust mental workload, enhancing patient engagement and recovery. Behavioural and Cognitive Sciences, Engineering and Technology, Information, Computing and Communication Sciences, Medical and Health Sciences Bachelor Thesis, Collaboration, Internship, Lab Practice, Master Thesis, Semester Project

Investigating Corticomuscular Coherence (CMC) in Cognitive and Muscular Fatigue during Dual Tasks University of Zurich Neuromorphic Cognitive Systems Group - Indiveri, Giacomo This project investigates the relationship between corticomuscular coherence (CMC) and both muscular and cognitive fatigue during dual-task performance. Using synchronized EMG and EEG data collected via the Quattrocento system from OTBioelettronica, we aim to explore CMC variations at the onset of these signals during solo and dual motor-cognitive tasks. By analyzing data from motor (e.g., reach-and-grab) and cognitive (e.g., reading) activities, we will develop algorithms for automatic detection of EMG and EEG signal onsets. The goal is to validate whether CMC coherence increases during dual tasks, contributing to interventions for fatigue management and cognitive-motor training. This research, in collaboration with IIT Genoa and led by Dr. Marianna Semprini, seeks to enhance our understanding of cognitive-muscular interactions through CMC. Engineering and Technology, Medical and Health Sciences Bachelor Thesis, Master Thesis