EEG

Context The brain-computer interface (BCI) that uses the code-modulated visual evoked potential (c-VEP) is one of the fastest non-implanted BCIs wordwide. A much used machine learning technique in c-VEP BCI is canonical correlation analysis (CCA).

Context Many conventional BCI systems adhere to fixed trial lengths. This allows users to issue commands only at predetermined intervals and using a constant amount of EEG data. Various more sophisticated methods have emerged to optimize trial duration dynamically, taking into account the non-stationary data.

Context A brain-computer interface (BCI) can use a diverse array of control signals, decodable from measured electroencephalogram (EEG) data. One such control signal is the code-modulated evoked potential, which is the response to pseudo-random sensory events.

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

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

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.