2016 International Conference on Brain Informatics & Health
October 13-16, 2016 in Omaha, Nebraska, USA

Topics of Interest

Track 1: Investigations of Human Information Processing Systems (HIPS) and Computational Foundations of Brain Science

  • Adaptation and self-organization;
  • Bayesian models of the brain, and causal modeling of behaviour for neurology;
  • Brain dynamics and functional/resting/structural networks;
  • Cognitive architectures and their relations to fMRI/EEG/MEG;
  • Computational mechanisms of learning and memory;
  • Computational models of sensory-motor control;
  • Conscious mental functions and subconscious information processing;
  • Emotion, heuristic search, information granularity, and autonomy related issues in reasoning and problem solving;
  • Higher-order cognitive functions and their relationships;
  • HIPS complex systems;
  • Investigating spatiotemporal characteristics and flow in HIPS and the related neural structures and neurobiological process;
  • Learning mechanisms (e.g., stability, personalized user/student models);
  • Methodologies for systematic design of cognitive experiments;
  • Models of executive function & prefrontal cortex;
  • Modeling brain information processing mechanisms (e.g., information organization, neuromechanism, mathematical, cognitive and computational models of HIPS);
  • Multi-perception mechanisms and visual, auditory, and tactile information processing;
  • Neural basis of decision-making;
  • Neural foundations of intelligent behavior;
  • Reasoning mechanisms (e.g., principles of deductive/inductive reasoning, common-sense reasoning, decision making, and problem solving);
  • Social brain communication.

Track 2: Information Technologies for Curating, Mining and Using Brain Big Data

  • Brain data collection, pre-processing, management, and analysis methodologies;
  • Brain connectome, functional connectivity, and multi-level brain networks;
  • Brain data grids and brain research support services;
  • Brain informatics provenances;
  • Brain mapping and visualization;
  • Cyber-individuals and individual differences;
  • Data brain modeling and formal conceptual models of brain data;
  • Databasing the brain, curating big data, and constructing brain data centers;
  • Development of data-driven markers of diseases, and behavioural biomarkers of neurological diseases;
  • fMRI and PET imaging registration and analysis;
  • Information technologies for simulating brain data;
  • Integrating multiple forms of brain big data obtained from atomic and molecular levels to the entire brain;
  • Knowledge representation and discovery in neuroimaging;
  • Large scale models and simulation of brains;
  • Machine learning algorithms for brain data analysis;
  • Measuring scale thresholds of brain big data;
  • Multi-aspect analysis in fMRI/DTI/EEG/ERP/MEG/PET/Eye-tracking data;
  • Multimedia brain data mining and reasoning;
  • Multimodal and combinatorial fusion for brain informatics;
  • Optogenetics and in-vivo cell imaging analytics;
  • Real-time fMRI and neurofeedback;
  • Remote neurological assessment;
  • Semantic technology for brain data integration;
  • Simulating and analysing spatiotemporal structure, characteristics and flows in HIPS and neural data;
  • Statistical analysis and pattern recognition in neuroimaging.

Track 3: Brain-Inspired Technologies, Systems and Applications

  • Affective computing and applications;
  • Brain/Cognition inspired computing and artificial systems;
  • Brain-computer interfaces and brain-robot interfaces;
  • Brains connecting to the Internet of Things;
  • Brain repair models and simulations;
  • Clinical diagnosis and pathology of brain and mind/mental-related diseases (e.g., mild cognitive impairment, alzheimers, dementia & neuro-degeneration, depression, epilepsy, autism, Parkinson's disease, and cerebral palsy);
  • Cloud and semantic brain data services;
  • Cognitive and decision support;
  • Computational approaches to rehabilitation;
  • Computational intelligence methodologies for mental healthcare;
  • Computational psychiatry;
  • Digital, data, and computational brain;
  • e-Science, e-Health, and e-Medicine;
  • Mental healthcare knowledge abstraction, classification, representation, and summarization;
  • Mental healthcare knowledge computerization, execution, inference, and management;
  • Mental health risk evaluation and modeling;
  • Neuro/Emotional robotics, computer vision and intelligent robotic networks;
  • Neuroeconomics and neuromarketing;
  • Neuroeducation, neurolinguistics, and neuroinstrumentation;
  • New cognitive and computational models of intelligent systems;
  • Non-verbal communication;
  • Personal, wearable, ubiquitous, micro and nano devices for mental healthcare;
  • Remote neurological assessment;
  • Social networks, social media, and e-learning for spreading mental health awareness;
  • WaaS (Wisdom as a Service) and active services for mental healthcare.