eegAnalysis-package: Feature selection and classification of electroencephalography data

Description

This package consists of a set of tools to classify electroencephalography (EEG) and to successfully reduce the feature space dimension. More specifically, this package contains functions to simulate data (randEEG), to train classifiers (svmEEG), to classify new data (classifyEEG) and to plot data (plotEEG and plotwindows). Nevertheless, what differentiates this package from others available in the community are the functions to automatically select the best features to use in the classification model (featureSelection and FeatureEEG).

Arguments

Details

Package:

eegAnalysis

Type:

Package

Version:

1.0

Date:

2014-04-08

License:

GLP (>=2)

References

Bostanov, V. (2004) BCI Competition 2003 - Data Sets Ib and IIb: Feature Extraction From Event-Related Brain Potentials With the Continuous Wavelet Transform and the t-Value Scalogram. IEEE transactions on biomedical engineering, V. 51, no. 6.

Brockwell, P.J., Davis, R.A. (2002) Introduction to Time Series and Forecasting. 2nd ed. Colorado: Springer. Cap. 4.

Coutinho, M. (2013) Selecting features for EEG classification and constructions of Brain-Machine Interfaces. Universidade de Brasilia (UnB), Master dissertation. Hastie, T., Tibshirani, R., Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Stanford: Springer.

Karatzoglou, A., Meyer, D., Hornik, K. (2006) Support Vector Machines in R. Journal of Statistical Software. Vol 15, issue 9.