1. What hardware is used to collect biofeedback and how is this data used?

Sensors are used to collect biometric data for the detection and quantifying of emotional reactions and triggers via wearable tech. We commonly use:

We use the obtained data for:

2. How do we detect and measure Affect?

Scientific literature provides evidence that frontal cortical EEG activation asymmetry predicts responsiveness to affective manipulations.

Tasks that include a response component will be more likely to show affect-related prefrontal cortex activation asymmetry in the dorsolateral regions, and its activity in these regions that are most likely to be reflected in scalp-recorded electrical brain signals. In response to cues that signal the requirement to inhibit a dominant response, the activation of the right-inferior and middle-frontal gyrus has been widely described.

The right ventromedial prefrontal cortex also appears to play a specialized role in the inhibition of impulsive affective urges. Patients with damage specifically to this prefrontal cortex sector on the right side, but not on the left side, have profound abnormalities in emotion-related decision making. It appears that this sector of right prefrontal cortex may be particularly sensitive to punishment so that when it is damaged; patients no longer have the usual cues that signal threat and danger and so tend to act impulsively.

However, with respect to the frontal EEG asymmetry literature, it is imperative that we be mindful of the fact that prefrontal cortex asymmetry represents only a small portion of the critical circuitry of emotion.

A particular pattern of EEG activation asymmetry has been found to predict reactivity to specific valence stimuli in a laboratory situation, and it’s related to the Alpha frequency band.

In neuroscience literature, the EEG pattern associated with food reward, on the other hand, is in the beta and theta rhythm localized to posterior-dorsal (parietal) cortex.

Our cross-validation takes into account both, enabling us to point out affect in our tested end users.

3. How do we detect and measure Engagement?

Spatial tasks engage the right hemisphere more than verbal tasks, with the exception of complex mental rotation, which produced an EEG pattern similar to verbal tasks. Most of the differences in EEG patterns between tasks were accounted for by differences in right hemisphere engagement. High left hemisphere engagement is related to mental rotation in scientific literature.

Regarding engagement measurement, we analyze alpha, beta and gamma frequency band and their correlation.

4. Why do we use biofeedback in market research and what do we measure?

The data gathered via biofeedback is a relatively new concept in market research, but nevertheless it is a concept which could revolutionize the way we gather data on product and brand desirability. Its potential uses are incredible, and it could contribute to once and for all resolving the age old problem in any market research ‒ socially desirable responses by the study participants.

In our studies, we gather biofeedback via four main variables: heart rate variability, heart rate, skin conductance and neurofeedback, which are in turn used to identify biophysical peaks that occur during testing.

Peaks in this sense could be defined as moments of increased emotional or cognitive processing, and the measures used provide a relatively robust and well-studied indication of the same.

5. Our methodology

We collect sensor data for various physiological signals such as: EEG, GSR, Heart Rate, temperature and optionally blood pressure. Our sensor collect EEG signals by a rate of 220 samples/sec from 4 channels ( fp1, tp9, tp10, fp2 by 10/20 system).

We apply various algorithms to:

Also, we calculate the nonlinear feature fractal dimension which represents the complexity of nonlinear and chaotic EEG signals. By performing frequency analysis on Heart Rate data, we obtain the Heart Rate Variability feature which is in strong correlation with the emotional state. With this obtained set of features, we train classification matrices with various classification algorithms such as FCM (fuzzy c-means) or k-NN (k nearest neighbors).

Our goal is to determine a matrix for each emotional class and to perform the classification for the incoming (testing) set of features.

6. Scientific papers that we use to back up our methodology

7. Is your personal information secure?

Yes. To protect your information, we use Amazon Cloud servers as our online platform.

Amazon Cloud takes reasonable precautions and follows the industry’s best practices to make sure your personal information is not inappropriately lost, misused, accessed, disclosed, altered or destroyed. All information you provide during the testing stays on the server and only our team can see it. Every respondent has an ID number that is visible on the results and there is no additional personal data attached to it.

8. Where is the data stored?

Your data will be stored on your mobile device and also securely stored on our cloud servers. All data is anonymized before being stored in the cloud. Unless you give us permission, no one will have access to the data stored on your device or in the cloud.

9. What do we do with the data? Can other people access this data?

All brainwave data is securely stored and is not publicly accessible. We follow strict ethics concerning data. Your data will remain strictly anonymous and confidential unless you give us explicit permission to the contrary.


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