An automated EEG repair tool Kristjan-Julius Laak mission Automate - PowerPoint PPT Presentation

An automated EEG repair tool Kristjan-Julius Laak

mission Automate the phase of cleaning EEG data from non-brain- related activity. impossible?

Step by step one goes very far (proverb)

Mission possible AUTOMATE ONLY SOME ASPECTS OF THIS PHASE

Who cares at all? 200 recordings .. 10 subjects ... 60 electrodes ....

!!!!!!!!!!!!!?!!!!!!!!!!!!!! 256

Goal Build an automated algorithm that detects and repairs channels containing noise or artifacts.

Prerequisites • Data is already preprocessed (Fieldtrip) • There are trials not a single continuous recording • Some aspects of the data are known (e.g. sample rate)

General pipeline Euclidean Probably ±250µV distance from density median estimate Visual ICA Interpolation rejection

General pipeline Euclidean Probably ±250µV distance from density median estimate Visual ICA Interpolation rejection

Euclidean distance from median

General pipeline Euclidean Probably ±250µV distance from density median estimate Visual ICA Interpolation rejection

Density estimate A. Probability density estimate plots for each channel per trial. B. The red line on is a reflection of the left side curve from the maximum, illustrating a Gauss curve.

General pipeline Euclidean Probably ±250µV distance from density median estimate Visual ICA Interpolation rejection

Bad channels (automated)

General pipeline Euclidean Probably ±250µV distance from density median estimate Visual ICA Interpolation rejection

General pipeline Euclidean Probably ±250µV distance from density median estimate Visual ICA Interpolation rejection

Independent component analysis

Results The difference between raw data (A) and pre-processed data (B) for the same trial. Different colours mark different channels.

Last slide.

Intentionally black slide... Thank you!