EEG

Posters, talks, and workshops

We are investigating a novel method for a gaze-independent visual brain-computer interface (BCI) for communication, which could potentially be used by users who cannot communicate anymore, for instance ALS patients. For this project, we are recruiting healthy participants aged 18-35 years.

Problem Many deep learning-based decoding methods have been developed in the past years for BCI applications with the aim of solving various challenges in the field. These challenges may include the ability to handle multiple EEG channel sets, to adapt to changing noise distributions in the data, to handle corrupted channels, or to calibrate using very few examples.

The Stroop effect is a well studied phenomenon in psychology. It is often used in cognitive neuroscience to study attention and cognitive control. In a typical experiment, subjects are presented with a word that names a color (e.

Collection of resources for generative models in BCI research

Context A brain-computer interface (BCI) harnesses a diverse array of control signals, decodable from measured electroencephalogram (EEG) data. An exemplary control signal within this realm is the code-modulated visual evoked potential (c-VEP), which manifests as a response to a pseudo-random sequence of visual flashes.

Context Typically, a brain-computer interface (BCI) necessitates users to direct their gaze towards a target stimulus, such as a symbol on a screen, as seen in a standard matrix speller. This gaze-dependent functionality, however, becomes progressively challenging for certain patient groups, notably individuals with amyotrophic lateral sclerosis (ALS).

Context A brain-computer interface (BCI) harnesses an extensive range of control signals decoded from measured electroencephalogram (EEG) data. Among the fastest BCIs for communication and control applications is the utilization of the code-modulated visual evoked potential (c-VEP).

Context A brain-computer interface (BCI) has the capability to utilize a diverse array of control signals decoded from measured electroencephalogram (EEG) data. One notably effective control signal for achieving both accuracy and speed in BCI performance, particularly in communication and control applications, is the code-modulated visual evoked potential (c-VEP).

While most machine learning methods make the assumption that data is i.i.d., the brain signal features collected within a brain-computer interface (BCI) experiment and even over multiple BCI sessions typically change over time.