Zero-Training for Brain-Computer Interfaces

Towards Zero Training for Brain-Computer Interfacing

Electroencephalogram (EEG) signals are highly subject-specific and vary considerably even between recording sessions of the same user within the same experimental paradigm. This challenges a stable operation of Brain-Computer Interface (BCI) systems. The classical approach is to train users by neurofeedback to produce fixed stereotypical patterns of brain activity. In the machine learning appro...

Towards Elaborated Feedback for Training Motor Imagery Brain Computer Interfaces

Motor imagery is one common paradigm in brain computer interface (BCI) systems where the user imagines moving a part of his/her body to control a computer. Motor imagery is endogenous and requires a large amount of training for the user to be able to control the BCI. Therefore, the feedback that is provided to the user is critical to ensure informative insight into improving imagery skills. In ...

True Zero-Training Brain-Computer Interfacing – An Online Study

Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user...

Semi-Supervised Classification for Intracortical Brain-Computer Interfaces Semi-Supervised Classification for Intracortical Brain-Computer Interfaces

Intracortical brain-computer interface (BCI) systems may one day allow paralyzed patients to interface with robotic arms or computer programs using their thoughts alone. However, a common and unaddressed issue with these systems is that due to small instabilities in the recorded signals, the decoding algorithms they rely upon must be retrained daily in a supervised manner. While this may be acc...

Metabolic Brain-Computer Interfaces