Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Past and Future of Multi-Mind Brain-Computer Interfaces

The great improvements in brain–computer interface (BCI) performance that are brought upon by merging brain activity from multiple users have made this a popular strategy that allows even for human augmentation. These multi-mind BCIs have contributed in changing the role of BCIs from assistive technologies for people with disabilities into tools for human enhancement. This chapter reviews the history of multi-mind BCIs that have their root in the hyperscanning technique; the collaborative and competitive approaches; and the different ways that exist to integrate the brain signals from multiple people and optimally form groups to maximize performance. The main applications of multi-mind BCIs, including control of external devices, entertainment, and decision making, are also surveyed and discussed, in order to help the reader understand what are the most promising avenues and find the gaps that are worthy of future exploration. The chapter also provides a step-by-step tutorial to the design and implementation of a multi-mind BCI, with theoretical guidelines and a sample application

Two Brains Guided Interactive Evolution

Abstract—In this paper, we show that it is possible to use electroencephalography (EEG) and multi-brain computing with two humans to guide an Interactive Genetic Algorithm (IGA) system. We show that combining neural activity across two brains increases accuracy to guide evolutionary search more effectively. The IGA system involves a simple task of evolving a polygon shape to approximate the shape of a target polygon. Two candidates visually inspected the evolved polygons and mentally ranked them (independently from each other) from 1−10 based on their similarity to the target polygon. In parallel, the IGA system evaluated the fitness of evolved polygons using a standard fitness function. The IGA system was run for a few generations, before evolution was paused and EEG signals were collected from the two candidates. The collected EEG signals were used to train a regression model that received unseen EEG as input and mapped this into fitness values. The trained model was then used to guide the IGA solely by using the EEG signals. Off-line experimental results showed that it was possible to build better regression models that are trained using two EEG signals to capture participants evaluation of fitness. This paper demonstrates the possibility of a new domain of applications for interactive evolution where standard fitness calculations can be replaced with multiple EEG signals for guiding an optimisation process. Index Terms—EEG, multi-brain, Interactive Genetic Algorithm. I