# Electroencephalography (EEG)

#### Exhibit 25.11   Electrode locations of for EEG recording according to the international 10-20 system. (Source: Wikipedia).

Electro-encephalo-graphy literally means writing of the electric activity of the brain. It is an imaging technique using electrodes placed on the scalp, which captures, amplifies and records the electric signals emitted by neurons in the brain.

The 10-20 system shown in Exhibit 25.11 is the internationally recognized standard for placement of electrodes. The names indicate the brain region or lobes where the electrodes are located: Frontopolar (Fp), Frontal (F), Temporal (T), Parietal (P), Occipital (O), and Central (C). The number or letter represents the hemisphere (odd = left hemisphere, even = right hemisphere), and the distance to the midline.

The actual distances between adjacent electrodes are either 10% or 20% (10-20 system) of the total front–back or right–left distance of the skull. The front–back measurement is taken from the nasion (depression between eyes and nose) to inion (bump at back of head), and the right–left measurement is from the right to the left preauricular points (just above the top of the earlobes).

Contemporary EEG devices such as the Fourier One (Exhibit 16.8) are lightweight, portable (wireless models permit mobility), and relatively inexpensive. They can provide for high time resolution, and compared to other imaging technologies, are better able to capture physiological changes in the brain.

EEGs are appropriate for capturing signals about attention, arousal, fatigue and surprise, which are emitted from the brain’s surface. They are not as effective in picking up signals from deeper within the brain, that are key for decision making. EEGs therefore are better suited for testing feelings and emotions, and not appropriate for testing informational content that requires thinking.

This section provides a brief description of how EEGs work. For a more details download EEG, The Complete Pocket Guide, by iMotions. The pocket guide is also a prime source for the information provided in this section.

### Frequency-based Analysis

Of the different ways that these synaptic waves are analysed by EEG devices, frequency-based analysis is of greatest relevance to consumer research. It is better suited for detecting emotions, thoughts and motivations in relation to the testing of advertising and packaging, product testing, and the design and navigation of websites.

The roughly 100 billion interconnected neurons in our brain constantly emit electric signals. These signals comprise a mixture of several underlying base frequencies, lying between 1 to 80 Hz that vary in magnitude (voltage). They are classified into frequency bands associated with different cognitive-affective states of mind:

1. Delta (1 – 4 Hz): These slow, high amplitude brainwaves are present only when the subject is in deep sleep, i.e. technically speaking, stage 3 or non-REM (non-rapid eye movement) sleep.
The stronger the delta rhythm, the deeper the sleep. Since memory consolidation occurs while we sleep, delta frequencies relate to biographic memory and procedural memory.
2. Theta (4 – 8 Hz): Oscillations in the theta range relate to mental workload and working memory. They correlate with the level of difficulty of the mental task.
3. Alpha (8 – 12 Hz): These frequencies correlate with mental and physical relaxation with eyes closed. Conversely, alpha blocking or suppression relates to activities with eyes open, when the brain is focussed and ready to absorb information.
4. Beta (12 – 25 Hz): Frequencies in this range correlate with active or anxious thinking that requires concentration. For instance, when the subject is thinking about executing movements demanding motor skills.
5. Gamma (above 25 Hz): This is a relatively grey area. There is lack of consensus among researchers as to what mental activities these frequencies relate to.

The analysis processing:

1. Clean and prepare the data.
2. Breakdown the total duration of the analysis into smaller time periods or epochs. For instance, a one minute advertisement is broken into 60 two-second epochs, each overlapping by 1 second.
3. #### Exhibit 25.12   Example of the EEG rhythms in frequency domain.

4. Fast Fourier Transformation (FFT) is applied to transform the EEG signals for the epochs into their frequency domain, and can be represented by frequency-amplitude charts similar to one in Exhibit 25.12. This data is captured across epochs, across electrodes and across respondents.
In terms of interpretation, strong frequencies reflect the subject’s cognitive-affective state.
5. Compute the metrics. Some of the better-known metrics include:
• frontal asymmetry index for approach-avoidance behaviour,
• cognitive state for engagement and distraction,

Plot the metrics across the epochs (time periods) to visualize how the state of mind varies over the duration of the stimulus. The data is usually averaged across respondents, and for an overall index, it is averaged over the epochs.

Some examples of the outputs of TVC, found on YouTube:

### Metrics

#### Exhibit 25.13   Physiological taxonomy of emotions. (Source: Advance Brain Monitoring).

The most common EEG metrics relate to approach-avoidance behaviour, cognitive state for engagement and distraction, and cognitive workload.

Details about some of these metrics are provided in this pdf. You may also want to visit Advanced Brain Monitoring from where the pdf was obtained, to learn about the company’s EEG devices and software.

At the moment, there appears not to be any standard nomenclature on the EEG metrics. Different service providers use different terms, but basically they all relate to the cognitive-affective states described earlier.

Exhibit 25.13 provides a depiction of physiological taxonomy of emotions where approach-avoidance varies along the x-axis and the cognitive state, along the y-axis.

### Frontal Asymmetry: Approach-Avoidance

#### Exhibit 25.14   Activity in the left/right-frontal brain reveal approach-avoidance tendencies. (Source: adapted from iMotions, image from Wikipedia).

The frontal asymmetry reveals insights into the subject’s motivations, emotions and engagement. As shown in Exhibit 25.14, increased activity in the left-frontal indicates positive feelings, engagement and motivation, and the reverse is true for the right-frontal. This relationship serves as a useful index of approach and avoidance:

$$Frontal \,Asymmetry \,Index \,(FAI) = log \left(\frac {alpha \,power \,right \,F4}{alpha \,power \,left \,F3}\right)$$

Higher frontal asymmetry index indicates positive disposition to stimuli whereas lower asymmetry indicates negative disposition. It is derived by extracting the signals from the electrodes F3/F4 and F7/F8.

Note that in addition to the FAI, the (12-25 Hz) beta or (> 30 Hz) gamma band power, particularly in the frontal cortical regions (electrodes F3 and F4), are good gauge for the motivation towards (approach) or away from (avoidance) any stimulus.

These metrics are very useful, not only for evaluating advertising and packaging, but also for product testing. They provide an unbiased indicator of the respondent’s emotional and motivational disposition towards a new product.

### Cognitive-Affective

Cognitive-affective metrics pertain to the cortical processes underlying mental workload or drowsiness. They provide a measure of the level of fatigue, attention, engagement and workload that the respondent’s brain is experiencing.

Cognitive States: Advanced Brain Monitoring’s (ABM) B-Alert EEG uses a scale ranging from 0 to 1 to gauge the extent that the respondent is tuned-out/attentive. There are 4 cognitive states on this scale — drowsiness & fatigue onset, distraction, low engagement and high engagement. Each of these states is characterized by different signals. The signs of drowsiness and fatigue for instance include higher delta band activity and theta bursts.

Workload: ABM also adopts a workload scale, also from 0 to 1, that gauges cognitive processes dealing with working memory, problem solving and analytical reasoning. There are 3 workload states — boredom, optimum and stress/information overload. This scale correlates with theta band activity; as theta power increases, so does workload.