title: A novel pattern mining approach for identifying cognitive activity in EEG based functional brain networks.
abstract: The complex nature of neuronal interactions of the human brain has posed many challenges to the research community. To explore the underlying mechanisms of neuronal activity of cohesive brain regions during different cognitive activities, many innovative mathematical and computational models are required. This paper presents a novel Common Functional Pattern Mining approach to demonstrate the similar patterns of interactions due to common behavior of certain brain regions. The electrode sites of EEG-based functional brain network are modeled as a set of transactions and node-based complex network measures as itemsets. These itemsets are transformed into a graph data structure called Functional Pattern Graph. By mining this Functional Pattern Graph, the common functional patterns due to specific brain functioning can be identified. The empirical analyses show the efficiency of the proposed approach in identifying the extent to which the electrode sites (transactions) are similar during various cognitive load states.
authors.affiliation: * Department of Applied Mathematics and Computational Sciences, Computational Neuroscience Laboratory, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India.
authors.forename: M
authors.initials: M
authors.lastname: Thilaga
authors.affiliation: * Department of Applied Mathematics and Computational Sciences, Computational Neuroscience Laboratory, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India.
authors.forename: R
authors.initials: R
authors.lastname: Vijayalakshmi
authors.affiliation: * Department of Applied Mathematics and Computational Sciences, Computational Neuroscience Laboratory, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India.
authors.forename: R
authors.initials: R
authors.lastname: Nadarajan
authors.affiliation: † Cognitive NeuroEngineering Laboratory, Division of Information Technology, Engineering and the Environment, University of South Australia, Adelaide, South Australia 5001, Australia.
authors.forename: D
authors.initials: D
authors.lastname: Nandagopal
mesh_terms: D001288:Attention / Q000502:physiology; D001307:Auditory Perception / Q000502:physiology; D001334:Automobile Driving / Q000523:psychology; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D003071:Cognition / Q000502:physiology*; D057225:Data Mining / Q000379:methods; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D008297:Male; D009434:Neural Pathways / Q000502:physiology; D009483:Neuropsychological Tests; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: EEG; Functional brain networks; cognition; graph pattern mining; graph theory; mutual information; network metrics