(BSc) thesis_project

For many machine learning methods in BCI, covariance matrices (more precisely: sample covariance matrix, SCM) are used to describe, how background brain signals auto-covary over time within a single measured EEG channel, and also how the signals of multiple channels cross-covary that are distributed over the scalp.

Brain-computer interface (BCI) protocols, which make use of an event-related potential protocol typically provide different external stimuli to users and record, if one of these stimuli was perceived as an attended target stimulus, while the other stimuli should have been ignored as so-called non-target stimuli.

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.

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.

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.