Frequency-based Analysis of Electroencephalography (EEG)

Electroencephalography (EEG) - Electrode locations of for EEG recording according to the international 10-20 system

Exhibit 15.11   Electrode locations of for EEG recording according to the international 10-20 system. (Source: Wikipedia).

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.

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. By analysing the different frequency bands of the brain's electrical activity, EEG can provide insights into the cognitive and emotional responses of consumers to marketing stimuli.

The frequencies are classified into these five bands:

  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 are associated with the formation of long-term memories, including 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, with theta power increasing as the task becomes more challenging. Theta activity is also associated with cognitive processing, attention, and memory retrieval. In consumer research, theta rhythms can be used to assess the level of cognitive effort required to process marketing stimuli such as product information, pricing, and promotional offers.
  3. Alpha (8 – 12 Hz): These frequencies correlate with mental and physical relaxation with eyes closed. When alpha activity is prominent, the brain is in a state of rest and is not processing any specific information. Conversely, alpha blocking or suppression relates to activities with eyes open when the brain is focussed and ready to absorb information. When alpha waves decrease, it indicates that the brain is engaged in active mental processing. Alpha rhythms are commonly used in consumer research to assess the level of attention and engagement with marketing stimuli such as advertisements, product packaging, and website designs.
  4. Beta (12 – 25 Hz): Frequencies in this range correlate with active or anxious thinking that requires concentration. Beta activity is associated with cognitive functions such as attention, focus, and working memory. It is also observed when the subject is thinking about executing movements demanding motor skills. In consumer research, beta rhythms can be used to assess the level of mental effort and engagement required to process marketing stimuli such as advertisements, product packaging, and website designs.
  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 can be broken down in the following stages:

  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. Electroencephalography (EEG) EEG rhythms in frequency domain

    Exhibit 15.13   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, which can be represented by frequency-amplitude charts similar to one in Exhibit 15.12. This data is captured across epochs, across electrodes and across respondents. By analysing the frequency distribution of the EEG signals, researchers can gain insights into the cognitive and emotional responses of consumers to marketing stimuli.
  5. Compute the metrics. Some of the better-known metrics include:
    • frontal asymmetry index for approach-avoidance behaviour,
    • cognitive state for engagement and distraction,
    • cognitive workload.
    More information about these metrics is provided in the next section.
  6. 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.

Electroencephalography (EEG) analysis case example Coca-Cola TVC

Exhibit 15.14   Coca-Cola TVC (Source: Neuro Discover.

Electroencephalography (EEG) analysis case example Carls Jr TVC

Exhibit 15.15   Carls Jr TVC (Source: Neurons Inc).

As can be seen from the findings of the studies on Coca-Cola and Carls Jr TVCs (shown in exhibits 15.14 and 15.15), the research combines EEG with eye tracking in order to determine which visuals are eliciting a higher degree of cognitive engagement and arousal. The EEG signals from the electrodes are aggregated into emotional states, such as arousal, motivation, and cognitive load, for the Carls Jr TVC. These emotional states are then tracked over the course of the advertisements, providing analysts with valuable insights into how viewers are responding to the content.

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