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