EOG Denoising

Summary

Doc_BoxAlgorithm_EOGDenoising.png
  • Plugin name : EOG Denoising
  • Version : 023
  • Author : Joao-Pedro Berti-Ligabo
  • Company : Inria
  • Short description : EOG Denoising using Regression Analysis
  • Documentation template generation date : Nov 2 2017

Description

Algorithm implementation as suggested in Schlogl's article of 2007

This box uses a denoising matrix 'b' calculated previously through the EOG_Denoising_Calibration for removing the EOG effects on EEG. The principle is based on regression analysis (see article 'A fully automated correction method of EOG artifacts in EEG recordings) where a matrix 'b' is estimated being:b = <'Nt N>-ยน<'N S> with N being the noise (EOG electrodes) and S the source (EEG electrodes). The signal output is the EEG_Corrected (free of EOG noise):O=S-b*N (EEG_Corrected = EEG - b*EOG)

Inputs

1. EEG

Make sure to select the same quantity of EEG channels as specified in your 'b' parameter matrix

  • Type identifier : Signal (0x5ba36127, 0x195feae1)

2. EOG

Make sure to select the same quantity of EOG channels as specified in your 'b' parameter matrix

  • Type identifier : Signal (0x5ba36127, 0x195feae1)

Outputs

1. EEG_Corrected

The output has the same structure as the EEG input

  • Type identifier : Signal (0x5ba36127, 0x195feae1)

Settings

1. Filename b Matrix

Make sure to select the right file containing your 'b' matrix with the right coefficients

  • Type identifier : Filename (0x330306dd, 0x74a95f98)
  • Default value : [ b-Matrix-EEG.txt ]

Examples

You can apply these boxes in a set with a high density of blinking eyes and see the results before and after.

Miscellaneous