Advanced data correction techniques
Despite all the precautions to obtain clean signals from EEG and MEG sensors, electrophysiological traces are likely to be contaminated by a wide variety of artifacts.
These include other sources than the brain and primarily the eyes, the heart, muscles (head or limb motion, muscular tension due to postural discomfort or fatigue), electromagnetic perturbations from other devices used in the experiment and leaking power line contamination, etc.
The key challenge is that most of these factors of nuisance contribute to MEG/EEG recordings with significantly more power than ongoing brain signals (a factor of about 50 for heartbeats, eye-blinks and movements, see Fig.3). Whether experimental trials contaminated by artifacts need to be discarded requires that these latter be properly detected in the first place.
The literature of methods for tackling noise detection, attenuation and correction is too immense to be properly covered in these pages. In a nutshell, the chances of detecting and correcting artifacts are higher when these latter are monitored by a dedicated measurement. Hence electrophysiological monitoring (ECG, EOG, EMG, etc.) is strongly encouraged in most experimental settings. Some MEG solutions use additional magnetic sensors located away from the subject’s head to capture the environmental magnetic fields inside the MSR. Adaptive filtering techniques may then be applied quite effectively (Haykin, 1996).
The resulting additional recordings may also be used as artifact templates for visual or automatic inspection of the MEG/EEG data. For steady-state perturbations, which are thought to be independent of the brain processes of interest, empirical statistics obtained from a series of representative events (e.g., eye-blinks, heartbeats) are likely to properly capture the nuisance they systematically generate in the MEG/EEG recordings. Approaches like principal or independent component analysis (PCA and ICA, respectively) have proven to be effective in that respect for both conventional MEG/EEG and simultaneous EEG/fMRI recordings (Nolte & Hämäläinen, 2001, Pérez, Guijarro, & Barcia, 2005, Delorme, Sejnowski, & Makeig, 2007, Koskinen & Vartiainen, 2009).
Modality-specific noise attenuation techniques, like signal space separation and alike (SSS), have been proposed for MEG (Taulu, Kajola, & Simola, 2004). They basically consist in designing software spatial filters that attenuate sources of nuisance that originate from outside a virtual spherical volume designed to contain the subject’s head within the MEG helmet.
Ultimately, the decision whether episodes contaminated by well-identified artifacts need to be discarded or corrected belongs to the investigator. Some scientists design their paradigms so that the number of trials is large enough that a few may be discarded without putting the analysis to jeopardy.