Paper Info
Title
Applying evolution strategies to preprocessing EEG signals for brain-computer interfaces
Abstract
An appropriate preprocessing of EEG signals is crucial to get high classification accuracy for Brain-Computer Interfaces (BCI). The raw EEG data are continuous signals in the time-domain that can be transformed by means of filters. Among them, spatial filters and selecting the most appropriate frequency-bands in the frequency domain are known to improve classification accuracy. However, because of the high variability among users, the filters must be properly adjusted to every user's data before competitive results can be obtained. In this paper we propose to use the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for automatically tuning the filters. Spatial and frequency-selection filters are evolved to minimize both classification error and the number of frequency bands used. This evolutionary approach to filter optimization has been tested on data for different users from the BCI-III competition. The evolved filters provide higher accuracy than approaches used in the competition. Results are also consistent across different runs of CMA-ES.
Year
DOI
Venue
2012
10.1016/j.ins.2012.05.012
Inf. Sci.
Keywords
Field
DocType
brain-computer interface,different run,classification accuracy,evolution strategy,bci-iii competition,appropriate frequency-bands,raw eeg data,high classification accuracy,appropriate preprocessing,classification error,preprocessing eeg signal,eeg signal,higher accuracy,brain computer interfaces
Frequency domain,Data mining,Computer science,Brain–computer interface,Preprocessor,Evolution strategy,CMA-ES,Artificial intelligence,Eeg data,Machine learning,Electroencephalography,Radio spectrum
Journal
Volume
ISSN
Citations 
215,
0020-0255
7
PageRank 
References 
Authors
0.46
13
3
Name
Order
Citations
PageRank
Ricardo Aler132135.25
Inés M. Galván2445.02
José M. Valls3515.97