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

Abstract

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.

Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs

“…Furthermore, the pre-designed algorithmic workflow may become entirely inappropriate if the task changes. On the other hand, most signal analysis-based channel selection methods focus on a single optimization objective, with task accuracy often chosen as the optimization goal in many algorithms (Rocha-Herrera et al, 2022 ). However, in addition to task accuracy, the number of selected channels is also a crucial metric when conducting channel selection.…”