  We present a new statistical method to analyze multichannel steady-state
local field potentials (LFP) recorded within different sensory cortices of
different rodent species. Our spatiotemporal multi-dimensional cluster
statistics (MCS) method enables statistical analyzing and comparing clusters of
data points in n-dimensional space. We demonstrate that using this approach
stimulus-specific attractor-like spatiotemporal activity patterns can be
detected and be significantly different from each other during stimulation with
long-lasting stimuli. Our method may be applied to other types of multichannel
neuronal data, like EEG, MEG or spiking responses and used for the development
of new read-out algorithms of brain activity and by that opens new perspectives
for the development of brain-computer interfaces.
