Online EEG artifact removal by adaptive spatial filtering

The performance of electroencephalography (EEG) data depends on the effective attenuation of artifacts that are mixed in the recordings. To address this problem, we have developed a novel online EEG artifact removal method for online applications, which combines Independent Component Analysis (ICA) and regression (REG) analysis. ICA-REG method relies on the availability of a calibration dataset of limited duration for the initialization of a spatial filter using ICA. Online artifact removal is implemented by dynamically adjusting the spatial filter in the actual experiment, based on linear regression.

This software can be used to do a pseudo-online artifact removal (necessary for the validations in your project), or a real-time filtering, or to filter the whole signal offline.

For support:

To cite this software:
Roberto Guarnieri et al 2018 J. Neural Eng. 15 056009

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