EEG Feature Extraction Using Evolutionary Algorithms for Brain-Computer Interface Development.

Brain-computer interfaces are systems capable of mapping brain activity to specific commands, which enables to remotely automate different types of processes in hardware devices or software applications. However, the development of brain-computer interfaces has been limited by several factors that affect their performance, such as the characterization of events in brain signals and the excessive processing load generated by the high volume of data. In this paper, we propose a method based on computational intelligence techniques to handle these problems, turning them into a single optimization problem. An artificial neural network is used as a classifier for event detection, along with an evolutionary algorithm to find the optimal subset of electrodes and data points that better represents the target event. The obtained results indicate our approach is a competitive and viable alternative for feature extraction in electroencephalograms, leading to high accuracy values and allowing the reduction of a significant amount of data.

Implementation and integration of image processing blocks in a real-time bottle classification system.

A practical solution to the problems caused by the water, air, and soil pollution produced by the large volumes of waste is recycling. Plastic and glass bottle recycling is a practical solution but sometimes unfeasible in underdeveloped countries. In this paper, we propose a high-performance real-time hardware architecture for bottle classification, that process input image bottles to generate a bottle color as output. The proposed architecture was implemented on a Spartan-6 Field Programmable Gate Array, using a Hardware Description Language. The proposed system was tested for several input resolutions up to 1080 p, but it is flexible enough to support input video resolutions up to 8 K. There is no evidence of a high-performance bottle classification system in the state-of-the-art. The main contribution of this paper is the implementation and integration of a set of dedicated image processing blocks in a high-performance real-time bottle classification system. These hardware modules were integrated into a compact and tunable architecture, and was tested in a simulated environment. Concerning the image processing algorithm implemented in the FPGA, the maximum processing rate is 60 frames per second. In practice, the maximum number of bottles that can be processed would be limited by the mechanical aspects of the bottle transportation system.

R2-Based Multi/Many-Objective Particle Swarm Optimization.

We propose to couple the R2 performance measure and Particle Swarm Optimization in order to handle multi/many-objective problems. Our proposal shows that through a well-designed interaction process we could maintain the metaheuristic almost inalterable and through the R2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approach is validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed algorithm produces results that are competitive with respect to those obtained by four well-known MOEAs. Additionally, we validate our proposal in many-objective optimization problems. In these problems, our approach showed its main strength, since it could outperform another well-known indicator-based MOEA.

A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms.

Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class.