Range of topics for Master's, Bachelor's and other theses

Our interdisciplinary research team is looking for dedicated students with experience in computer science, neurocognition, game development or related disciplines to develop real-time adaptive gaming systems. These systems adapt in real-time to the current state, actions and preferences of the player to provide an optimised and immersive gaming experience. Neurocognitive parameters are used as additional variables to integrate the player's mental state into the game mechanics.

This project is ideal for students interested in the combination of neuroscience and gaming.

Our research team in the field of Applied Neurocognitive Systems is looking for committed students of computer science or related disciplines to work on innovative software solutions for the NeuroLab. The aim is to develop tools and applications that optimise the acquisition, management and analysis of neurocognitive data and support the processes in the research laboratory.

This project is ideal for students interested in the interface between neuroscience and computer science. 

This data analysis project focuses on correlating electroencephalography (EEG) data with subjective reports collected during an experiment on human-robot interaction. The objective is to identify patterns and relationships between the neural responses and the participants’ self-reported experiences, such as trust and comfort with the robot. The student will work with an existing dataset, applying statistical and machine learning techniques to analyze the data. The project aims to provide insights into how subjective experiences are reflected in neural activity, ultimately contributing to the improvement of human-robot interaction models.

This project is ideal for students with a strong background in data analysis, neuroscience, and psychology, and an interest in the intersection of human-robot interaction and cognitive science. 

This data analysis project investigates the effects of mental rotation tasks within a virtual reality (VR) environment on working memory performance in autistic individuals compared to neurotypicals. The study aims to explore whether the immersive nature of VR influences cognitive load and spatial working memory differently across these two groups. The student will analyze the dataset of a VR-based mental rotation experiment conducted in autists and neurotypicals during which reaction times and error rates were recorded. The analysis will involve statistical comparisons of behavioral data and a review of the current literature on mental rotation and spatial working memory task in VR, with focus on autism research.

This project is ideal for students interested in psychology, VR technology, and autism research, particularly those with skills in data analysis and statistical methods. 

This project involves the development of a real-time heartbeat monitoring application that integrates with Microsoft PowerPoint. The application will capture live heart rate data from a wearable device (e.g., smartwatch) and display the data dynamically within a PowerPoint presentation. The project requires the design and implementation of software that can interface with the wearable device using Lab Streaming Layer (LSL), process the data, and update an animation integrated in a PowerPoint slide in real-time. The application is intended to be used for presentations in health-related fields, live demonstrations, or interactive sessions.

This project is ideal for students with skills in software development, particularly in integrating APIs, data processing, and PowerPoint automation. It is ideal for those interested in combining health technology with innovative presentation tools. 

This data analysis project aims to investigate the physiological arousal responses of autistic individuals compared to neurotypicals when exposed to vibro-tactile stimulation. Using electrocardiography (ECG) to measure heart rate and other related metrics, the project will assess differences in responses between the two groups. The student will analyze a dataset where participants were presented with vibro-tactile stimuli while their ECG data is recorded. The analysis will focus on comparing the arousal levels within and between participants, offering insights into processing of somatosensory stimuli. The project also includes a review of the current literature on ECG technology and autism research with focus on somatosensory stimuli.

This project is ideal for students with interests in psychophysiology, cognitive neuroscience, and biomedical signal processing, particularly those with experience in data analysis and statistics.

This student project focuses on leveraging error-related potentials (ErrPs) in EEG data to improve voice recognition systems in human-robot interaction (HRI). The goal is to test a paradigm where a robot can detect a specific user’s voice command from their neural responses to errors in voice recognition. The project will involve executing an experiment where participants are attempt to activate a robot by voice recognition, collecting EEG data to capture ErrPs when voice recognition errors occur, and using this data to refine the robot's voice recognition model. The outcome is expected to enhance the robot's ability to adapt to individual users, leading to more natural and effective interactions.

This project is well-suited for students with an interest in neurotechnology, machine learning, and robotics, particularly those with skills in signal processing and programming. 

This student project aims to evaluate the performance of mobile EEG devices in comparison to established lab-standard gel-based EEG systems. The project will involve conducting setting up a lab study to measure the accuracy, reliability, and usability of mobile EEG devices across various cognitive tasks. The student will collect data using both mobile and gel-based EEG systems, analyze the data for signal quality, and identify potential discrepancies between the two types of devices. The project also includes a review of the current literature on EEG technology and its applications, culminating in a comprehensive benchmarking report that could guide future developments in mobile EEG technology.

This project is ideal for students interested in neurotechnology, biomedical engineering, or cognitive neuroscience, with skills in data analysis and an interest in the practical applications of EEG systems. 

In this work, an existing data set will be analysed. The study dealt with the different perception of emotions between autistic and non-autistic people. Test subjects had to read various emotional stimuli and assess where on the body they felt the triggered emotions. The work is based on Nummenmaa (2013) https://www.pnas.org/doi/10.1073/pnas.1321664111.

This project is ideal for students from the fields of psychology, data science or cognitive science. An understanding of statistics is required. 

We also welcome unsolicited applications with your own project ideas from students who are passionate about a particular topic that falls within our thematic focus areas. If you have innovative ideas and creative approaches that could enrich our research profile, we invite you to share your proposals with us.

This is ideal for students who want to show a high level of initiative and can work independently.