A statistical method for analyzing and comparing spatiotemporal cortical   activation patterns
  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. 
Patrick Krauss, Claus Metzner, Achim Schilling, Konstantin Tziridis,   Maximilian Traxdorf and Holger Schulze
q-bio.QM q-bio.NC