EEG

Problem The “BERT moment” for EEG has arrived. Recent Foundation Models (such as LaBraM, BIOT, or BenDR) have been pre-trained on massive, diverse datasets to learn universal neural representations. While these models show good results on standard, public benchmarks, their “frontier” utility remains unclear.

Current brain-in-the-loop systems often rely on generic parameter configurations that overlook inherent neural variability. This research aims to improve system performance by implementing closed-loop-optimization to personalize parameters for each user and adapt them in real-time.

Deep learning for EEG decoding

Mother of All BCI Benchmarks

We are investigating efficacy of a scene-based stimulation in visual brain-computer interface (BCI) for communication. For this project, we are recruiting healthy participants aged above 16 years. Details about the experiment can be found here.

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