Abstract
Electroencephalography (EEG) is a fundamental diagnostic instrument for many neurological disorders, and it is the main tool for the investigation of the cognitive or pathological activity of the brain through the bioelectromagnetic fields that it generates. The correct interpretation of the EEG is misleading, both for clinicians’ visual evaluation and for automated procedures, because of artifacts. As a consequence, artifact rejection in EEG is a key preprocessing step, and the quest for reliable automatic processors has been quickly growing in the last few years. Recently, a promising automatic methodology, known as automatic wavelet-independent component analysis (AWICA), has been proposed. In this paper, a more efficient and sensitive version, called enhanced-AWICA (EAWICA), is proposed, and an extensive performance comparison is carried out by a step of tuning the different parameters that are involved in artifact detection. EAWICA is shown to minimize information loss and to outperform AWICA in artifact removal, both on simulated and real experimental EEG recordings.
Highlights
If the goal of our study is monitoring the ongoing electrical activity of the brain, it is likely that we will have to deal with electroencephalography (EEG)
Grunwald et al [17] presented a study about spontaneous facial self-touch gestures; the aim of the study was to investigate whether sFSTG are associated with specific changes in the electrical brain activity that might indicate an involvement of regulatory emotional processes and working memory
Artifact removal is unavoidably a lossy procedure; we must aim to reduce artifacts, saving most of the useful information embedded in the EEG
Summary
If the goal of our study is monitoring the ongoing electrical activity of the brain, it is likely that we will have to deal with electroencephalography (EEG). EEG is very noisy and often affected by artifacts, signals generated by artifactual sources (muscle movements, eye movements and eye blinks, sweating, breathing, heart beat, electrical line noise, etc.), whose electromagnetic fields mix with the bioelectromagnetic field generated by the brain during its activity, thereby corrupting it. In this way, the electrical activity of the brain might be partially or completely hidden, and EEG visual evaluation or EEG processing might provide incorrect results.
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