Data processing: From EEG to ERP

Recorded EEG comprises a mixture of the specific neural signals of interest and noise (i.e., general background EEG). Sources of EEG noise include artefacts associated with the eye balls (i.e., ocular artefacts), voltage drift and muscle activity, all of which must be minimised if the ERP signal of interest is to be clearly identified. The ERP signal is often very small and is easily overshadowed by larger changes in voltage generated by other concurrent brain activity and EEG artefacts. For this simple reason it is necessary to employ signal processing techniques to extract the signal from the background noise.

84 3.3.1. Averaging

As previously discussed in the introduction to this chapter, the ERP signal is extracted from the general EEG recording by averaging together EEG epochs time-locked to specific events of interest. There are, however, important implications of averaging that must be taken into consideration. Averaging assumes that the ERP signal has stable characteristics such as identical waveform morphology, amplitude and latency across trials. The signal of interest is generally very small compared to the background noise and as a result EEG typically has a poor Signal to Noise Ratio (SNR). When the time-locked event of interest is more highly correlated with the neural signal of interest than with the background noise, averaging will attenuate the noise and retain the signal;

thereby improving the SNR. In principle, the SNR increases as the square root of the number of trials averaged together (Perry, 1966). Consistent with many modern memory studies, this thesis required at least 16 good trials from each participant per condition to form an ERP, and any participant who did not provide 16 trials was excluded from the overall analysis.

Often neural activity is not perfectly correlated with the event of interest. In practice ERPs very rarely demonstrate stable characteristics across individual trials and the same cognitive process may not be engaged to the same degree for every trial. This problem can be moderated by simultaneously gathering behavioural responses and using these to exclude trials with incorrect responses. Even for the remaining correct trials, however, changes due to fatigue, boredom or lapses in attention will also introduce variance during the recording session. It is important to consider these problems when designing ERP experiments and interpreting results and as a result it is widely considered good

85 practice to limit the amount of time an experiment takes to complete as well as providing opportunities to take breaks.

Variability can also be introduced with temporal differences between trials. The epochs used to form ERPs are generally time-locked to a particular event of interest, such as the onset of a stimulus, to ensure that the cognitive process under examination is present in each trial. The amplitude peak associated with a particular cognitive process may, however, occur at different times across trials. When this ‘latency jitter’ occurs, the averaged signal will display a wider temporal distribution and smaller peak amplitude compared to the peaks elicited by individual trials (Rugg and Coles, 1995:

see Figure 3.6). To mitigate latency jitter, area amplitude measures can be used which are less susceptible to latency variability (although amplitude may still be reduced). The area under an averaged ERP component, for example, is equivalent to the average area of ERP components from individual trials. Area based measures, such as the mean voltage deflection over a particular time interval, are therefore almost always superior to peak-based measures. When using area based measures, however, it can be difficult selecting the time interval that accurately captures the component of interest, especially in the context of distinct but overlapping components.

86 Figure 3.6: The diagram illustrates the effect of latency jitter. Each panel shows individual trials and an averaged waveform. Although the same individual trial waveforms are illustrated in the left and right panels, the effect of latency jitter is illustrated in the left panel, distorting the peak amplitude of the average waveform (adapted from Luck, 2005).

In practice, many sources of noise do correlate with the event of interest and will not be attenuated to the same degree by averaging. Participants may blink or move their eyes, for example, every time a stimulus is presented. Any systematic sources of noise should ideally be identified, and compensated for, when designing an experiment or directly removed from the recorded data. There are multiple methods, for instance, that can be used to eliminate or compensate for noise associated with eye movements and blinks (discussed in detail in the following section). In general, an ERP component should be viewed as a record of all electrical activity correlated with an event and must be interpreted within this context.

Average Average

87 3.3.2. Ocular artefacts

One of the most common and systematic sources of noise contained in the EEG are generated by eye blinks and eye movements. Ocular artefacts can occur from muscle movements caused by eye blinks, but may also arise from the electrical gradient of the eye, which is positive at the front and negative at the back. As a result, eye movements can heavily distort EEG recordings. Both eye blinks and eye movements produce relatively large changes in potential at the scalp that can often mask smaller changes related to neural activity – especially at anterior scalp locations. Eye movements are typically measured with Electro-OculoGram (EOG). The EOG records differences in electrical potential between electrodes placed above and below one eye (Vertical EOG or VEOG) and between electrodes on the outer canthi to the left of the left eye and right of the right eye (Horizontal EOG, or HEOG).

There are three main methods of accounting for and removing ocular artefacts. First, one may limit the amount and severity of eye movements during the critical epochs by asking participants to constrain their blinks to set gaps between trials. Although effective, this method is often difficult for certain populations of participants including the elderly, the young and those who wear contact lenses. Additionally, asking participants to monitor their eye movements may introduce a secondary cognitive load that could potentially influence the EEG recording (Verleger, 1991). A much more common approach is to control eye movements indirectly by focusing the participants gaze onto the centre of the screen during critical epochs (using fixation crosses) and presenting stimuli on the screen within the focal area.

88 Secondly, researchers can identify and discard contaminated trials with excessive eye movements. Those trials that remain should then be free of ocular artefacts without introducing physical or cognitive noise associated with asking participants to monitor and suppress their own eye blinks/movements. One particular disadvantage of this approach, however, is the possibility of eliminating a large number of trials, which may severely reduce the overall power to detect a significant effect. Another issue is the relative difficulty in identifying excessive eye movements in every trial, resulting in a reduced and unknown contamination of the signal in the remainder of the trials.

The third and more common method is to correct for ocular artefacts rather than reject them. This approach allows for the retention of trials that contained ocular artefacts rather than rejecting trials. The method also avoids the issues associated with the requirement to suppress eye blinks/movements as previously discussed; for these reasons, the correction method is implemented in the current thesis. To be more specific, the correction procedure employed in this thesis applied a modelling technique that computes a regression coefficient for each electrode, allowing for a percentage of EOG activity to then be subtracted from every electrode. In most cases the subtraction will be more pronounced over the anterior scalp locations and less so across the central and parietal scalp areas. One potential limitation, however, is that eye electrodes detect neural activity (recorded from ocular electrodes) and subtraction of this mixed signal may lead to elimination of genuine effects. It is therefore important to take into consideration the advantages and disadvantages of each method for reducing and compensating for ocular artefacts when designing and implementing ERP experiments.

89 3.3.3. Saturation, voltage drift and additional artefacts

In addition to ocular artefacts, noise from other sources can also lead to contamination of the EEG recording. Slow voltage drifts in the signal, for example, are caused by changes in skin impedance brought about by rising temperatures in the recording chamber and slight changes in electrode position as a result of participant movement. It is important to bring down impedances before the experiment begins and to ensure that the participant remains as still as possible. Although high pass filters applied during recording go some way to attenuate these voltage drifts, they may still be evident in the recorded data. Voltage drift can be so large as to mask the effects of interest or even cause the signal to saturate by exceeding the input range of the digitiser. Although there are several methods of detecting drift, the current thesis uses a drift algorithm that identifies and eliminates any epochs in which one or more active electrode varied in amplitude by 75µV between the first and last data-point, over a period of 2000ms.

More high frequency sources of noise can be introduced by muscle activity, tension or surrounding electrical equipment. As with low frequency noise, the effects of high frequency noise can be reduced with low pass filters. Additionally, averaging techniques used to form ERPs are also effective in reducing the effects of high frequency noise. In some instances, however, the effects of high frequency noise still remain and it is important, as was done in the current thesis, to visually inspect the recorded EEG data for excessive muscle movement and reject contaminated epochs where necessary.

90 Visually inspecting the to-be-averaged epochs for other sources of noise such as signal saturation or recording artefacts is an effective way of eliminating additional noise from the data. Commonly, however, a final artefact rejection procedure is typically applied before averaging which systematically examines epochs of interest for large artefacts.

For instance, epochs containing any active electrode that deviated by more than a pre-defined amount, at any particular time during the epoch, will be rejected prior to forming ERPs.