Preprocessing pipeline

As already explained in previous sections, the passive shielding against noise is not enough to get rid of all the artifacts affecting MEG recordings.

In fact, some of the strongest artifacts are produced by the subject himself in the form of eye movements, head or muscular movements in general, heart beating, etc. As a consequence, it is crucial to process the data in order to remove, or correct those segments containing artifacts which masks brain activity. This procedure is generally called preprocessing and involves a set of steps that can vary between studies. The preprocessing pipeline undertaken in the three studies of this thesis is summarized in this section.

Spatiotemporal signal space separation method (tSSS)

The first step in the preprocessing pipeline is to apply the spatiotemporal extension of the SSS, the so-called tSSS, which was first introduced by Taulu & Simola (2006). The algorithm is included the Maxfilter v2.2.

software, native in Elekta systems, and is commonly applied in Elekta MEG recordings (Figure 1-13). SSS assumes the existence of three different volumes: a volume inside subject´s head containing brain currents, an outside volume containing all the currents producing external noise and a current-free intermediate volume containing the sensors. Sensor space signals are decomposed into series expansions of spherical harmonics. The algorithm then projects the components which were found to originate in the inner volume back to the sensor space. The spatiotemporal extension of this filter refines this version by adding the time dimension. Specifically, the algorithm looks for strong correlations between inside and outside components to remove those contributions as the inside components and the externals are expected to be unrelated and independent. It is important to highlight that SSS builds a single set of component using all 306 sensors (magnetometers and gradiometers together). Therefore, after SSS, the information contained in magnetometers and gradiometers is redundant. In

this thesis, all analyses after SSS were performed with the 102 magnetometer dataset. Artifact detection

Figure 1-13. Schematic representation of tSSS filter. Inner sphere,Sin (fitted to head center) contains the currents that are preserved after filtering. Sout is placed outside sensor space and contains the components removed by tSSS. sT is the space near the sensor space and does not contain electrical currents.

After denoising the raw MEG data using tSSS, the data were inspected to detect those segments containing artifacts. In particular, three categories of artifacts were identified (Figure 1-14).

1 - Muscular artifacts: Muscular artifacts are magnetic fields originated by muscle contractions. These artifacts typically show high amplitudes at higher frequencies (>30Hz). Although they are not restricted to a concrete sensor region they are more commonly found over temporal and frontal sensors, due to forehead muscle contractions and jawbone movements among others.

2 – Eye movements and blinks: These artifacts are also associated with muscle movements, although they are specifically associated with eye movements and blinks. Hence, they are typically found over frontal sensors.

Eye blinks can be detected in MEG raw data as sudden and discrete changes in amplitude. It is easy ensuring the identification of this type of artifacts due to the EOG channel. Their power spectrum is characterized by a power law distribution, with decreasing amplitude as frequency increases.

3 – Sensor jumps: Jump artifacts are due to failures in sensor electronics and are evidenced as abrupt changes in amplitude. They can be separated from blink artifacts because the latter show a softer shape with a progressive increase and decrease in power rather than a rectangular-like shape.

This artifact detection can either be done using automatized software or visually. In the three experiments included in this volume we employed an automatic procedure from the Fieldtrip toolbox (Oostenveld, Fries, Maris,

& Schoffelen, 2011) to detect signal segments containing artifacts.

Afterwards, these automatically detected artifacts were visually inspected to ensure the correct classification given the relatively high number of false positive detections.

Figure 1-14. Real MEG sensor activity showing typical artifacts. Left image: muscular artifact shown as high frequency and high amplitude oscillations. Middle image: blink artifact registered over frontal sensors and registered in the EOG channel (pink line bottom part). Right image: jump artifact produced by one sensor and spread over more channels because of tSSS signal reconstruction.

After removing all the artefactual segments, clean signal was divided into 4 seconds epochs including 2 more seconds at each side as padding to avoid edge effects in posterior band pass filtering. Only clean trials were used for all subsequent analyses.

Independent component analysis

Even after SSS, and removing notably artefacted segments, MEG signals still contain some noise. Independent component analysis (ICA) is a useful and popular technique to tackle this issue. It separates a multivariate signal into separate independent components. Many physiological and technical sources of noise separate into temporally independent components. This allow for the isolation and identification of the concrete contribution of these artefacts to the MEG signals. In particular, electrocardiogram (EKG), blinks, and ocular movements are usually well captured by ICA. In the three experiments a modification of the typical ICA procedure was employed to detect and remove the EKG component from the signal. This removes their contribution to the MEG signal without affecting the number of clean trials available.