Home // CENTRIC 2020, The Thirteenth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services // View article
Authors:
Aurora Saibene
Francesca Gasparini
Keywords: Brain Computer Interface; Electroencephalography; Evolutionary Feature Selection
Abstract:
Brain Computer Interfaces (BCIs) based on the recording of electroencephalographic signals have revolutionized the human-machine interaction. Being in presence of heterogeneous electrophysiological data, that come with a low number of instances and a great number of features, it is necessary to find a solution that can achieve good performances with respect to all the subjects, having as input a restricted feature subset. Firstly, we propose a population-based approach that allows to mitigate the data heterogeneity. Secondly, not wanting to make assumptions on the feature types, we propose the application of genetic algorithm, particle swarm optimization and simulated annealing as evolutionary feature selection techniques. We present the results of our proposal on a motor movement/imagery experiment. From these results, we verified that each feature type contributes differently depending on the task and on the sensor it was computed on, thus giving a broader view of the different type of analyses that can be performed to allow a better interaction between a user-centric system like a BCI based on motor imagery and its human user.
Pages: 8 to 14
Copyright: Copyright (c) IARIA, 2020
Publication date: October 18, 2020
Published in: conference
ISSN: 2308-3492
ISBN: 978-1-61208-829-7
Location: Porto, Portugal
Dates: from October 18, 2020 to October 22, 2020