Hybrid deep-learning model to recognise emotional responses of users towards architectural design alternatives

Figures & data

Figure 1. Application scenario.

Figure 2. Generative adversarial networks (GANs).

Figure 3. Deep neural networks (DNNs) structure.

Figure 4. Hybrid deep-learning model - GANs + EEG-based Deep-Learning Classification Model.

Figure 5. GANs generator and discriminator.

Figure 6. EEG-based Deep-Learning Classification Model.

Figure 7. (a) Subject recording EEG, (b) EmotivPro software interface with recording EEG (Emotiv 2018), (c) “EMOTIV EPOC+ 14 channel mobile EEG” device technical specifications (Emotiv 2018).

Figure 8. EEG-based Deep-learning classification model training dataset: (a) Features: 14 channels of recorded EEG data, (b) Affection: Re-classified PANAS Questionnaire Results, (c) Labels: “Negative” = 1 and “Positive” = 2, (d) Subject Initials, (e) Frequency (Hz).

Figure 9. Suggested Model Use Research Scenario.

Table 1. Comparison between real data and augmented data through iterations (Positive affection).

Iterations: 1,000	Iterations: 8,000	Iterations: 18,000	Iterations: 22,000

Emotiv. 2018. “EMOTIV EPOC+, EPOC+ Headset Details.” Accessed 27 September 2018. https://emotiv.gitbooks.io/epoc-user-manual/content/epoc+_headset_details/

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