Electroencephalographic (EEG) data is the electric potential change on the skull surface on a 100µV scale. These potential fluctuations are most likely produced by electrical fields generated by ion flux around axons of firing neurons. Axons that are oriented perpendicular to and not far from the skull surface contribute best to the signal. Around 10.000 neurons are required to fire in synchrony to obtain a good signal. But even contributions from deeper brain regions can not be excluded and a potential reversal can occur at electrodes near cortical folds. The measured signal is the superposition of all signals finally low-pass filtered by the skull.
4.1.1 EEG Recording
For clean recordings, a shielded recording environment is necessary. Electrical equipment should run on low DC current. Very sensitive difference amplifiers are used that feed the signal into the A/D converter at a recording computer. Electrodes are attached to the skull by an electrode cap that ensures a correct positioning. Electrical contact is maximized by degreasing the electrode sites with alcohol and by the application of a conductive gel until the impedance is below 5 kΩ.
The sintered ring electrodes are made of highly conductive material (Ag/AgCl) in our recording environment. In our case placing on the scalp follows the standardized extended 10-20 system by Jasper (1958), see figure 4.1. Electrodes are named according to their position from fronto-polar via anterior, frontal, central, parietal and temporal to occipital. Odd numbers are situated on the left side, even numbers on the right. Care has to be taken, when looking at head plot topographies, as the EEG-researcher looks at a head from above, whereas fMRI-researchers look
4 EEG Correlates of Negative Priming
Figure 4.1: The extended Ten-Twenty system of electrode placement as introduced by Jasper (1958).
at their subjects from the feet and thus sketch the head inversely. Additional electrode positions are located beneath the eye, a rather strong dipole, to record an electrooculogram in order to clean the data from collateral artifacts.
The reference for the difference amplifier is usually placed at a position where low brain activity can be expected, such as the mastoids TP9 and TP10 or a position above the great longitudinal fissure like FCz. Recording a difference signal has the advantage that global noise can be expected to be homogeneously present at all sites and thus not enter the data. Unfortunately global EEG signals do not as well. Choosing the reference is crucial as the signal of the reference is present in every time series. But if certain electrodes have been identified to contain a signal of interest, an offline rereferencing is still possible which can enhance the signal. In the raw recording setup we set a high sampling rate of 5000 Hz, applied a band pass filter between 0.1-70 Hz and a notch filter to suppress 50Hz mains hum.
A second computer is dedicated to stimulus presentation and recording of the behavioral data. It transmits time markers to the computer recording the EEG, ca. 4GB of data per session. Markers can be given for the onset of the successive stimulation displays and for the subjects’ responses and make a meaningful segmentation of the EEG data possible.
4.1.2 Data Processing
Before considering EEG data, the behavioral data is analyzed and outliers are rejected according to the following procedure. The outliers can be simple behavioral errors which invalidate the current trial and the successor, as the priming may be mixed up. Reactions that were faster than 250 ms or slower than 2000 ms were removed, as they most likely contain signals from processes differing from the ones under investigation. Finally, reaction times where the difference to the mean of the experimental condition exceeded two times the standard deviation were excluded too.
Because most inferential statistics assume a normal distribution of the data, Kolmogorov-Smirnov tests were conducted for the reaction times within the experimental conditions. If the test showed that the assumption of a normal distribution was violated, single values were removed based on their probability given the normal distribution model until the Kolmogorov-Smirnov test yielded insignificant results. Overall, we ensure that not more than 10% of the trials are excluded from the analysis for each participant. If this is the case, the subject is excluded completely from the analysis.
4.1 Introduction to Electroencephalography In order to obtain neural correlates of a certain behavioral effect, the effect has to be identified first by analysis of the behavioral data, i.e. by comparing average reaction times of the different priming conditions. Then the EEG is segmented according to the surveyed time markers. The segments should contain some information preceding the trial for a baseline correction as the EEG is no absolute measure but just potential differences. Also the reaction in each trial should be captured, thereby defining the segment length. Only segments belonging to behavioral valid trials enter the analysis.
The data still contains artifacts due to body movements, fluctuations in electrode conductance, electrostatic charges or technical errors. A major problem of recording the average activity of large numbers of neurons (as compared to intracellular measurements of single neurons) is the very strong background activity in the data, apparent as very strong noise. In the EEG, noise is significantly stronger than the ERP itself (Flexer, 2000). Whereas the electrical background activity is in the range of 1−200µV, the evoked potentials have an amplitude of only 1−30µV (see figure 4.2).
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Figure 4.2: Noise and signal in EEG-data. Note that the approximate amplitude of the average ERP of 132 trials is in the order of 10µV, while single trial recordings show amplitudes of≈100µV.
Thus a cascade of data cleaning and trial rejecting procedures is carried out. Basic techniques are simple filters, baseline corrections and visual inspection and manual removal of erroneous elec-trodes or segments. Automatic data rejection excludes trials with a maximum potential surpassing a certain threshold. Furthermore, eyeblinks cause major disturbances in the data, especially in the fronto-polar regions. Their impact has to be removed either by traditional regression based techniques (Gratton et al., 1983) or by more recently developed approaches using independent component analysis (Joyce et al., 2004; Jung et al., 2000; Delorme et al., 2007).
After these cleaning procedures, segments of one experimental condition are averaged pointwise forming the averaged event-related potential (AERP). The AERP usually shows a stereotypical form, a sequence of minima and maxima. Height and latency of the peaks, which are named according to polarity and sequential number, alternatively approximate latency, as N2, P3 or P300, are assumed to contain information about cognitive processes. Therefore latency and amplitude of the peaks are systematically compared between the priming conditions in order to determine which mechanisms in the trial processing carry the difference between the conditions.
4 EEG Correlates of Negative Priming