Aphasia Rehabilitation

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.

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.

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.