Figure 2.

Schematic view of EEG2Beh(avior) and the identified. Subjects move their fingers to actively sense a surface while their brain activity (e.g. EEG signals) r_i(t) is recorded. The relevant kinematic features of the sensorimotor behavior (the movement velocity here) are extracted, resulting in a time series s(t). An optimization procedure, implemented via canonical correlation analysis, then computes spatial filters w to apply to the neural signals and temporal filters h(t) to apply to the velocity such that the resulting filter outputs are maximally correlated in time. The algorithm output is a set of multiple EEG-kinematic components and their coupling strengths ρ². Three pairs of EEG components (scalp maps of neural activity) and their matching kinematic components (temporal profiles of velocity filters) were found to show significant correlations.