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.

*
Learning to repair artifacts in brain signals

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

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

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

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).

Estimating ERP Classification Performance from Oscillations

Estimating ERP Classification Performance from Oscillations

Context Many experimental protocols for brain-computer interfaces (BCIs) present a sequence of stimuli to the user. By attending one type of stimulus and by ignoring other stimuli, so-called target and non-target responses are evoked, which can be measured by M/EEG and decoded by the BCI.

Language-training induced oscillatory effects in stroke patients with aphasia

Language-training induced oscillatory effects in stroke patients with aphasia

Context Left-hemispheric stroke can lead to different language deficits, which are a heavy burden both, for the individual and his/her social interaction, as well as for society. While spontaneous recovery in the first weeks after the stroke may reduce this so-called aphasia, many patients remain with different language problems also in the chronic phase.

Channel set invariance for neural networks

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.

Decoding motor performance from non-neural data

Decoding motor performance from non-neural data

Our group developed a fast assessment of motor performance for Parkinson patients, the CopyDraw task. This paradigm enables us to record multiple repetitions of Parkinson patients performing the task with and without deep brain stimulation.

Decoding of hand force from EEG recordings

Decoding of hand force from EEG recordings

Context For the rehabilitation training after stroke, simple repetitive hand motor tasks have been developed. One of them, the Sequential Visual Isometric Pinch Task (SVIPT) has been performed in studies with healthy users and stroke patients while the user’s brain activity (electroencephalogram, EEG) has been recorded.

Hand motor performance decoding using slow potentials

Context Simple repetitive hand motor tasks, such as those used for rehabilitation training after stroke, are known to show strong trial-by-trial performance variations even though a trial typically lasts a few seconds only.

Phonological scoring of non-standard speech

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

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.