Electroencephalography measures brain activity in real time. Artifacts are spurious signals due to eye movements and blinking, muscular/cardiac activity, and generic electrical disturbances. Artifacts have to be recognized and eliminated to allow a correct interpretation of the useful brain signals (UBS). Independent component analysis (ICA) is an effective strategy to retrieve the independent components (IC) of the signals. The reprojection of those components on 2D topographies of the scalp (topoplots) facilitates the recognition of artifacts from UBS. In this chapter, we describe the preprocessing pipeline based on the topoplot analysis of ICs. Several ICA algorithms are introduced, both for offline and online artifact recognition. In particular, we discuss several automatic methods that, emulating the human vision approach, are capable of recognizing all the common artifacts with the same precision as human experts. Finally, as a case study, we chose one of them and described its pipeline in detail. The chosen architecture, composed of a modular ensemble of 2D convolutional neural networks, is capable of recognizing all the most common artifacts in an online mode.
CNN-based artifact recognition from independent components of EEG signals
Placidi, Giuseppe
2024-01-01
Abstract
Electroencephalography measures brain activity in real time. Artifacts are spurious signals due to eye movements and blinking, muscular/cardiac activity, and generic electrical disturbances. Artifacts have to be recognized and eliminated to allow a correct interpretation of the useful brain signals (UBS). Independent component analysis (ICA) is an effective strategy to retrieve the independent components (IC) of the signals. The reprojection of those components on 2D topographies of the scalp (topoplots) facilitates the recognition of artifacts from UBS. In this chapter, we describe the preprocessing pipeline based on the topoplot analysis of ICs. Several ICA algorithms are introduced, both for offline and online artifact recognition. In particular, we discuss several automatic methods that, emulating the human vision approach, are capable of recognizing all the common artifacts with the same precision as human experts. Finally, as a case study, we chose one of them and described its pipeline in detail. The chosen architecture, composed of a modular ensemble of 2D convolutional neural networks, is capable of recognizing all the most common artifacts in an online mode.File | Dimensione | Formato | |
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