title: Using conditional FCM to mine event-related brain dynamics.
abstract: We introduce a framework for mining event related dynamics based on conditional FCM (CFCM). For a given set of responses, the variation in the data is summarized by means of a small set of meaningful prototypes accompanied with a low-dimensional graph capturing their relative relationships. CFCM enables prototyping in a principled manner. User-defined constraints, which are imposed by the nature of experimental data and/or dictated by the neuroscientist's intuition, direct the process of knowledge extraction and can robustify single-trial analysis. The method is introduced using simulated data and demonstrated using actual encephalographic data.
authors.affiliation: Artificial Intelligence & Information Analysis Laboratory, Department of Informatics, Aristotle University, Thessaloniki, Greece. chzigkol@csd.auth.gr
authors.forename: Christos N
authors.initials: CN
authors.lastname: Zigkolis
authors.forename: Nikolaos A
authors.initials: NA
authors.lastname: Laskaris
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D015225:Magnetoencephalography / Q000379:methods; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated