Automatic feature selection for BCI: An analysis using the davies-bouldin index and extreme learning machines

In this work, we present a novel framework for automatic feature selection in brain-computer interfaces (BCIs). The proposal, which manipulates features generated in the frequency domain by an estimate of the power spectral density of the EEG signals, is based on feature optimization (with both bina...

Full description

Saved in:
Bibliographic Details
Main Authors: Coelho, G. P., Barbante, C. C., Boccato, L., Attux, R. R. F., Oliveira, J. R., Von Zuben, F. J.
Format: Conference Proceeding
Language:English
Subjects:
Artificial Immune Systems
Brain Computer Interfaces
Davies-Bouldin Index
Electroencephalography
Extreme Learning Machines
Feature extraction
Feature Selection
Filtering algorithms
Indexes
Machine learning
Optimization
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this work, we present a novel framework for automatic feature selection in brain-computer interfaces (BCIs). The proposal, which manipulates features generated in the frequency domain by an estimate of the power spectral density of the EEG signals, is based on feature optimization (with both binary and real coding) using a state-of-the-art artificial immune network, the cob-aiNet. In order to analyze the performance of the proposed framework, two approaches are adopted: a direct use of the Davies-Bouldin index and the use of metrics associated with the operation of an extreme learning machine (ELM) in the role of a classifier. The results reveal that the proposal has the potential of improving the performance of a BCI system, and also provide elements for an analysis of the spectral content of EEG signals and of the performance of ELMs in motor imagery paradigms.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2012.6252500