Signal acquisition and analysis
A telemetric Nautilus EEG system (g.tec, Schiedlberg) was used to
acquire ECG and EEG signals according to a modified International 10-20
System. In details, leads were placed in F3, F7, C3, T7, P3, PO3, F4,
F8, C4, T8, P4, PO4, Fz, Cz, Pz, Oz positions and referred to Cz. Two
additional electrodes were placed on the skin near lateral cantus of
left eye and in correspondence of left orbital ridge to detect eye
movements. Another electrode, referred to Cz, was placed in proximity of
left shoulder for ECG signal. All impedances were kept below 5 kΩ.
The ECG signal was analyzed with Kubios HRV (Tarvainen et al., 2014).
First, the RR series were extracted from the ECG using the well-known
Pan-Tompkins algorithm (Pan & Tompkins, 1985) and a cubic spline
interpolation method was used to correct algorithm-related
peak-detection artefacts. The obtained RR series were then resampled to
4Hz to derive the HRV signal (Malik, 1996). Starting from the HRV, we
extracted several features in the frequency and time domain.
Specifically, we computed the mean RR interval duration (RR, ms), the
total RR variability (SD) the square root of the mean squared
differences of successive normal-to-normal (NN) intervals (RMSSD), the
power expressed as a percentage of total power in the low-frequency (LF,
0.04–0.15 Hz) and high frequency (HF, 0.15–0.40 Hz) ranges, the ratio
of LF to HF power (LF/HF ratio). R peaks markers were saved for
subsequent HEP analysis.
EEG signals were analyzed using EEGLAB (Delorme & Makeig, 2004) and
MATLAB 2020b (The Mathworks, Inc., 2020) custom scripts. All EEG signals
were downsampled to a sampling frequency of 125Hz after applying a
proper low-pass anti-aliasing filter. Then, a high-pass filter of 0.1Hz
was applied. Bad channels were removed through a semi-automatic
procedure. First, the channels whose correlation coefficient with their
neighbors was lower than a predefined threshold were discarded (here set
to 0.8) (Mullen et al., 2013). Then, an expert visually inspected the
data to eventually identify and remove those bad channels not captured
by the correlation criterion. Afterwards, removed channels were
recovered using a spline-spherical interpolation method. The obtained
signals were re-referenced to the numeric average of all channels before
undergoing to independent component analysis (ICA) (Makeig et al.,
1995). ICA decomposes the signals into sets of maximally statistically
independent components that represent both brain sources and different
type of artefacts (e.g., muscular, ocular) (Onton et al., 2006).
Artefact-related ICs were identified through visual inspection of their
maps, spectra and time-course (Delorme et al., 2012). Each independent
component was additionally described through its associated equivalent
current dipole (Oostenveld, 2011). Finally, the EEG signals were
reconstructed without the contribution of artefact-related ICs (i.e,
ocular, muscular, cardiac, channel noise and other sources of
artefacts). The HEP amplitude was obtained by averaging the EEG signals
synchronized the R-peaks previously obtained with Kubios (Tarvainen et
al., 2014). More specifically, we extracted EEG epochs from -200 ms to
600 ms around each R-peak followed by subtractive baseline correction
estimated in 200ms preceding the R-peak. The epochs contaminated by
abrupt signal changes in the ECG or EEG signal were also discarded from
the analysis. At the end of this procedure, we obtained a collection of
EEG epochs for each condition for each subject (% of retained epochs
> 95%).