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 : Dec 30 2016
  • WARNING : this box has been marked as UNSTABLE by the developer. It means that its implementation may be incomplete or that the box can only work under well known conditions. It may possibly crash or cause data loss. Use this box at your own risk, you've been warned.

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