Join us for thesis projects, lab rotations, internships!

We welcome applications by visiting PhD students and PostDocs, and from students of the Radboud University who want to do projects in our lab. We can offer projects for

• lab rotations
• internships
• thesis projects (BSc and MSc). Most projects are centered around the development of machine learning algorithms for the decoding of brain signals or encoding of brain states. Compared to other types of data, these time-series signals pose specific challenges. We develop algorithms to cope with these challenges in order to conduct fundamental research or to translate the algorithms into clinical and non-clinical neurotechnological applications. Well-known examples are brain-computer interfaces (BCI) for communication and control, BCI-supported rehabilitation approaches, adaptive closed-loop deep brain stimulation and novel human-robot interaction paradigms.

Prerequisites

The projects in our lab mostly have a data-driven focus, such that a solid background in machine learning / artificial intelligence, math and good programming skills (Python) is a must for most projects. The individual project descriptions listed below may provide further details.

External Students

For applications from external Master or PhD students, the following factors must be taken into account:

1. We have no means to provide funding for the duration of the project. Living costs in Nijmegen are approx. 1200 EUR per month.
2. As the overhead to ramp up into a research topic is substantial, we can only accept external students who stay 6 months at least.

Example Topics

To tickle your fancy, we have listed a few ready-to-go projects. Upon request, we can also tailor projects to your background and interest, given it has some relevance for our research goals.

Interpersonal physiological synchrony to monitor attention in a group

Context EEG signals in different individuals attending to the same narrative or movie clip have been found to be more similar to each other when they are attending this stimulus compared to when they are not.

No interpolation for topoplots

The mne library offers a builtin plotting option for what is commonly referred to as topo plots. These plots take a single value per EEG/MEG channel and try to show the distribution across the scalp by proving a ventral (top -> down) view of the channel positions and interpolating values in between to create a quasi-head-shaped heatmap.

Learning to repair artifacts in brain signals

Electric and magnetic brain signals recordings (M/EEG, sEEG, LFP) are prone to a contamination by so-called artifacts, which do not have a neural origin but are caused by sensor problems, external electro-magnetic sources, or by bio-signals such as muscle activity.

Auditory and tactile code-modulated BCI

Context A brain-computer interface (BCI) can use a multitude of control signals that are decodable from measured EEG. An example of such a control signal is the code-modulated visual evoked potential (c-VEP).

Gaze-independent c-VEP BCI

Context Typically, a brain-computer interface (BCI) still requires its user to move the eyes towards a target stimulus (e.g., a character) on a screen (e.g., in a standard matrix speller), specifically, they are gaze-dependent.

Stimulus characteristics of c-VEP BCI

Context A brain-computer interface (BCI) can use a multitude of control signals that are decodable from measured EEG. One of the control signals that leads to accurate and fast BCI performance for instance for communication and control, is the code-modulated visual evoked potential (c-VEP).

Channel set invariance for neural networks

Problem Despite the well-established standards for EEG electrode layout like the 10-20 montage, EEG recordings obtained in different labs or across studies are not straightforward to compare. There are usually important differences between the datasets, but also within them.

Phonological scoring of non-standard speech

Context While deep learning approaches have significantly pushed language applications like keyword spotting in audio recordings, natural language processing on text documents etc., these solutions for the masses can not applied directly to non-standard speech signals, e.

Reinforcement learning under high noise and uncertainty

Context Reinforcement learning (RL) methods interact with a system in closed loop in order to learn suitable action strategies. The goal of this interaction is to control the system, e.g., to bring it into a desired state.