R: Toolkit for Electroencephalography Data

eegkit-package {eegkit}

R Documentation

Toolkit for Electroencephalography Data

Description

Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for (i) plotting EEG data, (ii) filtering EEG data, (iii) smoothing EEG data; (iv) frequency domain (Fourier) analysis of EEG data, (v) Independent Component Analysis of EEG data, and (vi) simulating event-related potential EEG data.

Details

The DESCRIPTION file:

Package:	eegkit
Type:	Package
Title:	Toolkit for Electroencephalography Data
Version:	1.0-4
Date:	2018-11-06
Author:	Nathaniel E. Helwig <helwig@umn.edu>
Maintainer:	Nathaniel E. Helwig <helwig@umn.edu>
Depends:	eegkitdata, bigsplines, ica, rgl, signal
Description:	Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for (i) plotting EEG data, (ii) filtering EEG data, (iii) smoothing EEG data; (iv) frequency domain (Fourier) analysis of EEG data, (v) Independent Component Analysis of EEG data, and (vi) simulating event-related potential EEG data.
License:	GPL (>=2)

Index of help topics:

eegcap                  Draws EEG Cap with Selected Electrodes
eegcap2d                Draws 2D EEG Cap
eegcapdense             Draws Dense EEG Cap with Selected Electrodes
eegcoord                EEG Cap Coordinates
eegdense                Dense EEG Cap Coordinates
eegfft                  Fast Fourier Transform of EEG Data
eegfilter               Filters EEG Data
eeghead                 Dummy Head for 3d EEG Plots
eegica                  Independent Component Analysis of EEG Data
eegkit-package          Toolkit for Electroencephalography Data
eegmesh                 EEG Cap for Dense Coordinates
eegpsd                  Plots Power Spectral Density of EEG Data
eegresample             Change Sampling Rate of EEG Data
eegsim                  Simulate Event-Related Potential EEG Data
eegsmooth               Spatial and/or Temporal Smoothing of EEG Data
eegspace                Plots Multi-Channel EEG Spatial Map
eegtime                 Plots Single-Channel EEG Time Course
eegtimemc               Plots Multi-Channel EEG Time Course

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

Maintainer: Nathaniel E. Helwig <helwig@umn.edu>

References

Adler, D., Murdoch, D., and others (2014). rgl: 3D visualization device system (OpenGL). http://CRAN.R-project.org/package=rgl

Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Begleiter, H. Neurodynamics Laboratory. State University of New York Health Center at Brooklyn. http://www.downstate.edu/hbnl/

Bell, A.J. & Sejnowski, T.J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7, 1129-1159.

Cardoso, J.F., & Souloumiac, A. (1993). Blind beamforming for non-Gaussian signals. IEE Proceedings-F, 140, 362-370.

Cardoso, J.F., & Souloumiac, A. (1996). Jacobi angles for simultaneous diagonalization. SIAM Journal on Matrix Analysis and Applications, 17, 161-164.

Cooley, James W., and Tukey, John W. (1965) An algorithm for the machine calculation of complex Fourier series, Math. Comput. 19(90), 297-301.

Harrell, F., Dupont, C., and Others. Hmisc: Harrell Miscellaneous. http://CRAN.R-project.org/package=Hmisc

Helwig, N. E. (2013). Fast and stable smoothing spline analysis of variance models for large samples with applications to electroencephalography data analysis. Unpublished doctoral dissertation. University of Illinois at Urbana-Champaign.

Helwig, N.E. (2018). bigsplines: Smoothing Splines for Large Samples. http://CRAN.R-project.org/package=bigsplines

Helwig, N.E. (2018). ica: Independent Component Analysis. http://CRAN.R-project.org/package=ica

Helwig, N. E., Hong, S., Hsiao-Wecksler E. T., & Polk, J. D. (2011). Methods to temporally align gait cycle data. Journal of Biomechanics, 44(3), 561-566.

Helwig, N.E. & Hong, S. (2013). A critique of Tensor Probabilistic Independent Component Analysis: Implications and recommendations for multi-subject fMRI data analysis. Journal of Neuroscience Methods, 213, 263-273.

Helwig, N. E. & Ma, P. (2015). Fast and stable multiple smoothing parameter selection in smoothing spline analysis of variance models with large samples. Journal of Computational and Graphical Statistics, 24(3), 715-732.

Helwig, N. E. & Ma, P. (2016). Smoothing spline ANOVA for super large samples: Scalable computation via rounding parameters. Statistics and Its Interface, 9(4), 433-444.

Ingber, L. (1997). Statistical mechanics of neocortical interactions: Canonical momenta indicatros of electroencephalography. Physical Review E, 55, 4578-4593.

Ingber, L. (1998). Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG. Mathematical Computer Modelling, 27, 33-64.

Oostenveld, R., and Praamstra, P. (2001). The Five percent electrode system for high-resolution EEG and ERP measurements. Clinical Neurophysiology, 112, 713-719.

Schlager, S. & authors of VCGLIB. (2014). Rvcg: Manipulations of triangular meshes (smoothing, quadric edge collapse decimation, im- and export of various mesh file-formats, cleaning, etc.) based on the VCGLIB API. R packge version 0.7.1. http://CRAN.R-project.org/package=Rvcg.

Singleton, R. C. (1979) Mixed Radix Fast Fourier Transforms, in Programs for Digital Signal Processing, IEEE Digital Signal Processing Committee eds. IEEE Press.

Examples

# See eegcap, eegcapdense, eegfft, eegica, eegresample, 
#     eegsim, eegsmooth, eegspace, eegtime, and eegtimemc