1. Consumer Neuroscience EEG+EYE Tracking
Indroneel Chatterjee

2. Paper Discussion
Consumer Neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram and eye tracking.
Khushaba et al, 2013

3. Consumer Neuroscience
Interdisciplinary field combining psychology, neuroscience and economics. Often referred to as Neuromarketing.
Consumers cannot fully explain their preferences when asked explicitly due to non-conscious processes driving human behaviour
Rising interest of Neuromarketing over time and quickly being replaced by the word Consumer Neuroscience Companies Worldwide exclusively into Neuromarketing.

4. Case Studies/Examples
Amount of cortical spectral activity from frontal and parietal regions was higher for remembered TV advertisements compared to forgotten or unseen. (Ohme et al, 2010).
Sadness yields a pattern of large exchange of information in the frontal cortical channels while happiness yields a larger synchronisation among frontal and occipital sites. ( Costa et al, 2006)
Relation between EEG signals and human emotions while watching movies more important in the alpha, beta and gamma rather than delta and theta bands. ( Nie et al, 2011)
Frontal Alpha Asymmetry Studies: Higher alpha activity in right side of prefrontal cortex on exposure to emotional stimuli. The opposite for informational stimuli.

5. THE EXPERIMENT
DCE experiment : cognitive & emotional neural data plus preference data collected.
Three different crackers presented at once from a set of :
1) Shape: Square, Circle or Triangle
2) Flavour: Wheat, Dark Rye, Plain
3) Topping: Salt, Poppy, no topping
Total of 57 choice sets created with 3 displays each . Participants had to indicate their most and least favourite.
Each response is decomposed into separate preferences for each cracker feature. Also, EEG recording indicates changes in EEG spectral activity.

6. The Equipment
EEG : Emotiv EPOC : 14 Channels,. According to system. Two electrodes above ears : CMS/DRL references for L&R.
Eye Tracker: Tobii X60 : flat surfaces, Accuracy – 0.5, error – 15 pixels avg. drift factor less than 0.3 and sampling rate – 60Hz.
Forced to start at the same time by synchronisation software written in Visual Basic, after collection data transferred to Matlab.

7. The Stimuli Sequence of 57 Choices generated.
Each varied in shape, flavour and topping.
Three crackers features varied to produce full factorial design producing 27 crackers.
Balanced incomplete block design to assign 27 crackers to 57 choice sets.
Controls for order of appearance, equal appearance and co-appears equally often.
PARTICIPANTS :
18 , mixed gender, ( avg 28 years) years of age, right &Left mixed.
Eye tracker recalibrated every time upon adjustment.
Average time taken to complete experiment : 7 minutes.

8. Data Analysis
Cleaning and Denoising: Combination of ICA (Baseline removal, filtering, 50Hz) and DWT.
EEG Spectrum analysis : power +phase.
Each participant spent different amounts of time on the choice sets which reduced upon familiarity.

9. Data analysis
For power spectral analysis the EEG segments corresponding to the participants moving the hands to click on the mouse and time after making choices were not included.
Moving average spectral analysis of preferences related EEG data was accomplished using epochs of EEG data of various lengths.
Each EEG epoch corresponding to each of the 57 choices was analysed using 128-point window with 64-point overlap.

10. Contd..
Less than 128 points EEG frame was extended to 128 points by Zero Padding to calculate its power spectrum using a 256 point FFT.
This results in a power-spectrum density estimation with a frequency resolution near 0.5 Hz.
Average Power Spectrum of all the sub-epochs within each epoch was calculated in the Delta, Theta, Alpha, Beta and Gamma bands.
The averaged power spectrum of each epoch was normalised to a logarithmic scale to linearize the multiplicative effects.

11. |Contd…
The Power spectrum features in all bands was extracted including the mean of all.
Patterns of interdependency between different brain regions as participants looked at different characteristics of the crackers was investigated.
Since the magnitude of FFT of the EEG signals was used to detect interdependence between power and preferences- the same is employed to directly quantify ( typo?) frequency-specific synchronisation between two EEG signals.
PLV ( Phase Locking value ) was used as a measure of Synchrony defined at a time ‘t’.
PLV calculated indicates which brain regions and which EEG bands are mostly getting phase synchronisation for each of the bands.
Therefore this approach detects interdependencies between the power in each of these bands at each channel with preferences for : shapes, flavours and toppings of the crackers.

12. Results
Frontal Channels and Occipital Channels were most Synchronised. Dynamic cooperation between cortical areas for information exchange during emotional responses ( Costa et al, 2006)
Results show the importance of Theta, Alpha and Beta which reflected the highest PLV at the frontal and occipital regions while studying preferred vs non preferred marketing stimuli ( Aurup, 2011, Custudio, 2010)
O1 & O2 highest sync with P7 & P8 instead of frontal channels as indicated before (Costa et al,2006) – difference could be in nature of the task – crackers vs emotional video.
Thus results mention that phase synchronisation provides important data in understanding decision making tasks related to subjective preferences.

13. Results…
Part 2 : assessing individual preferences for each cracker characteristic decomposed into 3 characteristic levels and verified by eye tracker.
Each characteristic decomposed into binary vectors. For example – value of selected Square =1 vs other cases when unselected =0. Similarly for other two shapes. Same coding used for flavours and toppings.
Each extracted EEG provides one choice for one cracker and not the other two as the sampling is insufficient to get reliable measures.
High mutual information value between EEG features and preferred labels mean the corresponding attribute had a high impact on magnifying EEG power in specific band.

14. Band Wise Results :
Alpha:
Alpha power agreed with theta in left occipital region. Alpha showed high Mutual dependence at left frontal and left temporal regions ( Fig 10.c). Associated with preference studies ( Custdio, 2010)
Alpha on F3 received higher MI on impact of flavours and toppings as compared to shape preferences.
ANNOVA indicated significant alpha band differences at F3, T7 and O1.
Beta:
Beta band : Left Occipital region , Bilateral frontal regions (FC5 & FC6) and the left frontal region ( F3) . Findings support that flavour and topping had larger impact on preference than shape due to higher MI values.
Gamma exhibited high flavour and topping preferences on bilateral frontals and left temporal regions. May be due to familiarity with visual stimuli and degree of preference for it . ( Golumbic et al, 2008).
Overall, importance of frontal, temporal and occipital regions is reinstated while forming preferences for flavour and toppings. ANNOVA tests confirm significant differences across the Beta and Theta power features with a P value <.001 individually.

15. Results…
Delta
In Delta : changes in MI values with different characteristics were more in the left frontal region ( F3, FC5) than right regions ( F4, FC6).
Right temporal and anterior frontal ( T8 & AF4 ) higher MI than T7 &AF3.
Delta Oscillations as a signature of stimulus elicited activity in the brains reward circuit ( Stefanics et al, 2010). Indicates the importance of left frontal regions and right temporal regions to the choice task. ANNOVA in Delta band from different EEG Channels ( P<0.05)
Significant differences between Delta Band features in the Left ( AF3, F3, FC5, T7) and right hemispheres ( AF4, F4, FC6, T8) with associated P values <0.001
Theta :
Highest level of MI over left occipital region and partially bilaterally over frontal regions (F3 & F4) ( fig 10.b) .
Left occipital region is associated with encoding of visual stimuli ( Hald et al, 2006) Processing of semantically coherent or violated sets of cracker characteristics)

16. Conclusion
Neuromarketing and consumer neuroscience research helps in understanding consumer responses without relying on explicit data collection.
Restriction in translation to the actual experience of food consumption, can be used for packaging and advertising.
Contributions to understanding regions of the cortex and the different bands of neural activity associated with visual processing and decision making .

17. BIBLIOGRAPHY
Source : Khushaba, R. N., Wise, C., Kodagoda, S., Louviere, J., Kahn, B. E., & Townsend, C. (2013). Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Systems with Applications, 40(9),
Available at :