Studying the relationship between the brain and cognitive processes with

In this doctoral thesis, electroencephalography (EEG) will be the main method to investigate the relationships between cognitive functions and brain activity. Electrical potentials are generated by activations of large populations of neurons. When placing a couple of electrodes on the scalp, the electrical potentials can be measured by the differences between one electrode and a reference electrode. The voltage differences over time are referred to as EEG signals (Coles & Rugg, 1995; Gazzaniga, Ivry, & Mangun, 2009). In 1875, Caton (1875) first reported electrical brain activity from the skulls of mammals, which paved the way for the future research on rhythmic activity in EEG. In 1929, Berger first reported rhythmic EEG activity from the human scalp (Bastiaansen, Mazaheri, & Jensen, 2012). From then on, EEG has become a popular functional neuroimaging technique to investigate how neural activity supports cognitive processes. The difference in voltages between an electrode site and a reference site measured from the scalp is a summation of activity of a large group of neurons that are activated synchronously and share similar spatial directions (Coles & Rugg, 1995). In order to generate currents that can be detected on the scalp, the neurons have to be aligned in a parallel direction. If the directions of the neurons are random, the current flow can be cancelled out from each other. A dipolar field with a group of parallel neurons is known as ‘open fields’ (Coles & Rugg, 1995). However, if the sources of electrical fields are too far away from the scalp such as the thalamus, even ‘open fields’ might not be able to be detected on the scalp (Coles & Rugg, 1995). It is suggested that post-synaptic potentials of cortical pyramidal neurons instead of axonal action potentials are the main generators of scalp EEG signals because pyramidal neurons have

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a larger population and more sustained synchronisations in the same geometrical orientations than axons (Coles & Rugg, 1995; Luck, 2005).

The amplitudes of scalp EEG signals are usually in the order of microvolts. To convert such tiny analog signals into digital form, EEG recording systems always have amplifiers. An Anolog-to-Digital (AtoD) converter transforms analog waveforms into digits that can be stored and manipulated in the computer. The digital resolution of an AtoD converter is a power of 2 (Picton et al., 2000). For example, the resolution of the AtoD converter used in this doctoral thesis was 12 bits, which means the analog EEG voltages are represented by 212 or 4096 values. The gain of an EEG recording system can be calculated by the factor of the amplification and the resolution of the AtoD converter (Picton et al., 2000). For example, if the amplification factor is 20,000, the range of the AtoD converter that signals are blocked is ±5 V, the resolution of the system can be calculated as 10/20,000/4096, which is 0.122 µV/bit. The digital data points can be reconstructed into waveforms that reflect real voltages of EEG signals.

As digital data points that are converted by an AtoD converter are discrete, how many data points can be converted from a continuous EEG waveform depends on how many samples there are in one second, that is, the sampling rate. According to the Nyquist theorem, the sampling rate must be at least twice the highest frequency in the data (Luck, 2005). For example, if the highest frequency of interest is 35 Hz (within Gamma frequency band) then the sampling rate must be more than 70 Hz.

However, there might be some high frequency noise such as 50 Hz line noise while sampling. In that case, a low-pass filter will help to cut off the noise higher than a selected frequency without aliasing problem caused by slowing down the sampling rate (Luck, 2005). At the same time, some low frequency artefacts such as skin potentials caused by sweat might result in saturation of the digital signals. To avoid such problems, a high-pass filter can be used to cut off the drifts slower than a selected frequency.

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However, filters might lead to distortion in amplitudes and latency of the data (Luck, 2005). In the studies in this doctoral thesis, online band pass analog filters were between 0.01 Hz and 35 Hz, which removes high-frequency noise such as 50 Hz but might still leave some muscle artefacts and some slow drifts. However, these band pass filters allow looking at slow modulations and most ERP activity such as subsequent memory effects and oscillatory activity in theta (4 - 8 Hz) and alpha (8 - 12 Hz) frequency bands.

In cognitive neuroscience research, EEG activity is recorded while human subjects do tasks that include presenting a series of stimuli. Usually, EEG activity will be extracted into epochs. Each epoch might be time-locked to the stimulus and segregated by the manipulations of the tasks or the stimulus. Such event-related EEG signals can be analysed by two approaches: ERP and time-frequency analyses. Compared to fMRI, EEG has poorer spatial resolution and it is hard to interpret where the intracranial sources are. What is observed on the scalp is the summation of neural activity from different parts of the brain. However, EEG has a temporal resolution in milliseconds, which allows dissociating brain activity before a stimulus from that after the stimulus (Otten et al., 2006; Rugg et al., 2002; Wagner et al., 1999). In this doctoral thesis, pre-stimulus and post-stimulus encoding-related activity will be investigated by EEG and analysed with both ERP and time-frequency approaches. ERPs have been widely used to investigate cognitive processes since the 1960s. Recently, attention to oscillations has increased (for a review, see Bastiaansen et al., 2012). Both approaches will be generally introduced in this chapter.