2.1. Deep learning for design

Regarding the purpose of this study, it is worth discussing the overlap between design science and computational creativity. Computational creativity refers to a system that exhibits behaviours that unbiased observers would deem to be creative (Colton & Wiggins Reference Colton and Wiggins2012). Since deep learning has become more prevalent and powerful in the computer science field, systems have become more intelligent and able to complete creative tasks, such as visual art, poetry, music and design (Loughran & O’Neill Reference Loughran and O’Neill2016; Chen et al. Reference Chen, Wang, Dong, Shi, Han, Guo, Childs, Xiao and Wu2019). By summarizing perspectives from psychology, philosophy, cognitive science and computer science as to how creativity can be measured both in humans and in computers, Lamb et al. (Reference Lamb, Brown and Clarke2018) make recommendations for how to evaluate computational creativity from perspectives including person, process, product and press. This is in line with the purpose of our research, as we attempt to reveal the implicit connection between person and product by investigating whether a human’s preference can be embodied in AI designs. In previous GAN based AI design research, for example, the approach for design ideation by Chen (Reference Chen, Wang, Dong, Shi, Han, Guo, Childs, Xiao and Wu2019), human’s judgement is mainly involved in the post process of AI generation, which results in inappropriate evaluation in terms of computational creativity.

Several deep neural network approaches for image generation have been proposed recently, such as natural image generation (Brock et al. Reference Brock, Donahue and Simonyan2018), human face generation (Karras et al. Reference Karras, Laine and Aila2018) and the neural style transfer model (Gatys et al. Reference Gatys, Ecker and Bethge2016; Johnson et al. Reference Johnson, Alahi and Fei-Fei2016; Li & Wand Reference Li and Wand2016; Zhu et al. Reference Zhu, Park, Isola and Efros2017), which can generate images which contain the content of the given image with style features from the artistic images. Isola et al. (Reference Isola, Zhu, Zhou and Efros2017b) investigated the image transfer problem which generates new images from photos and applied also to human-drawn sketches. Karras et al. (Reference Karras, Laine and Aila2018) proposed an image-to-image translation method which translated an image from a source domain X to a target domain Y (using unpaired examples). An image compositing method was proposed by Luan et al. (Reference Luan, Paris, Shechtman and Bala2018). This copied an element from a photo and pasted it into a painting while maintaining spatial and inter-scale statistical consistency. Dong et al. (Reference Dong, Yu, Wu and Guo2017) explored semantic image manipulation by generating realistic images from an input source and a target text description that not only match the content of the description but also maintain text-irrelevant features of the source image. Elgammal et al. (Reference Elgammal, Liu, Elhoseiny and Mazzone2017) used creative adversarial networks to automatically generate artwork by maximizing the deviation from established styles and minimizing the deviation from art distribution. In a more high-level exploration, researchers have started to apply deep learning in auto design generation. Yu et al. (Reference Yu, Dong, Wang, Wu and Guo2018) proposed DesignGAN to generate a shape-oriented bionic design that maintains the shape of the design target and combines the features from the biological source domain. Also inspired by bionic design, Duncan et al. (Reference Duncan, Yu, Yeung and Terzopoulos2015) presented a method for generating zoomorphic shapes by merging a man-made shape and an animal shape. One method employed by Bernhardsson (Reference Bernhardsson2016) generates font designs by walking through their latent space. Sbai et al. (Reference Sbai, Elhoseiny, Bordes, LeCun and Couprie2018) use a generative adversarial learning framework to generate inspirations for fashion design, creating original and compelling fashion designs to serve as an inspirational assistant.

In addition to the direct image generation technology summarized above, there are also some methods considered to improve the quality of an image, such as the image inpainting method investigated by Liu et al. (Reference Liu, Reda, Shih, Wang, Tao and Catanzaro2018), which could fill in ‘holes’ in an image. This uses partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels. Also, the image colourization method was investigated by Nazeri et al. (Reference Nazeri, Ng and Ebrahimi2018), which could generate an image with plausible colours based on the adversarial learning framework. Some approaches have enabled the development of design applications, for example, Prisma (Anon Reference Anonn.d.), a photo editor that turns a photo to an artwork.

However, these approaches mainly focus on automatically generating new art and design images with the features from input images. A problem with this type of generative creativity is the postgeneration evaluation since the generation is completely random. How to make a selection from a large number of automatically generated designs remains a challenge. The user is a crucial part of the traditional design process; therefore, consideration of human aspects in the design process is essential, which is missing in the current auto AI design generation approaches. How to generate a desirable design with the preference from clients is a key question in our research. To integrate human aspects into the design process, we explored neuroscience-inspired design and a deep learning framework conditioned on brain signals is described in the next two subsections.

2.2. Current neuroscience-inspired design

Noninvasive methods for measuring human brain activity that have been developed include EEG, fMRI and NIRS. EEG measures subcranial electrical signals from electrodes in contact with the scalp. Neuroscience has inspired many developments in design, such as understanding cognitive neurofeedback from clients, building and developing new products and evaluating advertizing. For example, neuroimaging has been used in understanding packaging design to help explain how packaging design confuses the consumer (Basso et al. Reference Basso, Robert-Demontrond, Hayek, Anton, Nazarian, Roth and Oullier2014). Velasco et al. (Reference Velasco, Woods and Spence2015) have presented an experimental research programme on evaluating the impact of different orientation of design elements in product packaging. Furthermore, to understand the consumer psychology of a brand, Plassmann et al. (Reference Plassmann, Ramsøy and Milosavljevic2012) have reviewed the applications of marketing and also describe issues for future research. In a review of neuroscience-inspired design (Spence Reference Spence2016), one problem of commercial neuromarketing was noted that the results provided by neuroimaging are a clear answer to a ‘black-and-white’ question rather than a discriminating analysis of a ‘shades of grey’ question. Inspired by this review, the potential of introducing neuroscience into a deep learning framework has been explored, where the machine could not only provide a response to a ‘black-and-white’ question but also show other potential visualizations relating to a ‘shade of grey’ intuition.

2.3. Brain signal conditioned deep learning framework

Machine learning methods have been applied to both EEG and fMRI to help understand visual images, for example, Bashivan et al. (Reference Bashivan, Rish, Yeasin and Codella2015) proposed an approach for learning the representation from multichannel EEG time-series. Spampinato et al. (Reference Spampinato, Palazzo, Kavasidis, Giordano, Souly and Shah2017) have developed a visual object classifier driven by human brain signals. Distinct from Spampinato et al. (Reference Spampinato, Palazzo, Kavasidis, Giordano, Souly and Shah2017) who used EEG data, Horikawa et al. explored object decoding from fMRI patterns (Horikawa & Kamitani Reference Horikawa and Kamitani2017), which shows that the latent representation of real images (CNN1-8, HMAX1-3, GIST and SIFTbBOF) can be predicted from the fMRI signals. Both of these EEG and fMRI results show the potential of brain-based information retrieval. Furthermore, researchers have tried to generate related visual information from the decoded information of brain signals. To decode a brain image from EEG signals, Palazzo et al. (Reference Palazzo, Spampinato, Kavasidis and Giordano2017) have combined GAN with a recurrent neural network model to process EEG signals and reconstructed the viewing images of participants. Kavasidis et al. (Reference Kavasidis, Palazzo, Spampinato and Giordano2017) proposed a method for generating images using visually evoked signals recorded through EEG. In addition to EEG, fMRI signals are also widely used. Shen et al. (Reference Shen, Horikawa, Majima and Kamitani2019, Reference Shen, Dwivedi, Majima, Horikawa and Kamitani2018) have successfully demonstrated that visual images can be reconstructed from decoded fMRI signals. An unsupervised model using variational autoencoder to model and decode fMRI activity in the visual cortex was proposed by Han et al. (Reference Han, Wen, Shi, Lu, Zhang, Fu and Liu2019). This work showed the possibility of projecting both images and corresponded fMRI signals into latent spaces.

These generative brain decoding methods provide inspiration to explore a new method for design cognitive analysis which takes into account human brain activity. However, these methods are focusing on a brain decoding approach, aiming at reconstructing the mental image of what people think about. There is a lack of exploration of generating a design image with consideration of human cognition. Previous research has explored the reconstruction of seen images but the principles could also be relevant to explore human imagination.

4.1. Participants and equipment

The EEG study included six right-handed student volunteers (three females and three males) aged between 17 and 30 years old, with normal or corrected-to-normal vision. All participants gave informed consent to take part in the EEG experiment and had considerable training in EEG experiments. Our EEG recordings were performed using an electrode cap with 64 Ag/AgCI electrodes which were mounted according to the extended international 10/20 system. An online 50-Hz notch filter was added to avoid power line signal contamination.

Signals were recorded by using a Neuroscan Synamp2 Amplifier (Scan 4.3.1; Neurosoft Labs Inc., Sterling, VA) and sampled at 1000 Hz. Eye blinks were recorded from left supra-orbital and infra-orbital electrodes, whereas the horizontal eye movement EEG was recorded from electrodes placed 15 mm laterally to the left and right external canthi. The forehead (AFZ) was used for the ground electrode, and the reference electrode was attached to the left mastoid. All electrode’s impedances were maintained below 5 kΩ.

4.2. Visual stimuli

In this experiment, the stimuli consisted of five different categories of product images (handbag, headset, mug, watch and guitar) from ImageNet (Fei-Fei et al. Reference Fei-Fei, Deng and Li2010), which are widely recognizable and common products to help ensure the participants had similar familiarity with the stimuli; each category included 50 images. The size of the pictures was resized to 500 × 500 pixels and cropped to the centre of the screen.

4.3. Experiment design

Two separate data collection sessions were conducted consisting of an image presentation experiment and the preference imagery session. The data collected from the image presentation session are used for model training and those collected from the preference imagery session are used in the model utilization stage. In order to ensure the quality of the data, an electrode connection checking session was added before each run. During the experiment, the subject was accommodated in a sound-attenuated and electrically shielded room and seated comfortably. The stimuli images were presented in the centre on the screen and at a fixed distance. In addition, a press button pad was provided for the subjects to give feedback during the experiment. Subjects were able to stop the experiment at any time.

In the image presentation session (Figure 3), five categories of images were presented in five runs, each run consisting of one category of 50 images and separated in five blocks, each block with 10 different images and one repeated image. The subjects were required to view the images and press the button on the board when they saw the repeated images to maintain their attention. At the beginning of each block, a fixation red cross was presented in the central of the screen for 1000 ms. At the end of each run, 3000 ms were added as a rest time. In the preference imagery session (Figure 4), the subjects were required to visually imagine their preferred products with a prompt such as ‘Imagine a bag you like’ and follow the instructions that appear on the screen. This session consisted of five runs and each run contained 10 blocks. First, a fixation red cross was shown in the centre of the screen for 1000 ms. After this, the instruction was presented in the middle of the screen, and the subjects were asked to visualize the preferred visual look of the product. Following an audible beep, they were asked to close their eyes for an 8-s imagination period. After this, the subjects were required to evaluate the correctness and vividness of their mental imagery on a 5-point scale (Very vivid, Fairly vivid, Rather vivid, Not vivid and Cannot correctly recognize the target) by pressing the button of the box. The items evaluated as ‘Cannot correctly recognize the target’ are removed from the dataset. In the end, subjects were also required to draw down the image they imagined after each block. A total of 3000 ms refreshing time was added before and after each block. The subjects could stop the experiment at any time during the experiment.

Figure 3.

Image presentation experiment. Images were presented in the centre of the display with a central fixation cross. Ten images were shown per-block with one repeated image which required subjects to press a button when saw this image to maintain their attention.

Figure 4.

Preference imagery experiment. The onset of each block was started by a central fixation cross. The 8000 ms imagery periods were signalled by auditory beeps. Before the first beep, subjects were required to visualize the preferred product for 4000 ms as the preparation of the imagery after. At the end of each block, subjects were required to evaluate the vividness of their imagination by pressing the button.

After we obtained the raw data, the data were preprocessed by EEGLAB. The preprocessing procedure includes four stages, the channel selection stage, the epoch extraction and remove baseline stage, the rejecting artefacts stage and a data filtering stage. The channel selection was aimed at rejecting some bed signal channels which may influence data analysis. Then we extracted epoch according to the event markers and removed the baseline by subtracting the value of the first data from the original data. In the rejecting artefacts stage, we run both artefact correction (Zeng et al. Reference Zeng, Song, Yan and Qin2013) and independent component analysis (Zeng et al. Reference Zeng, Song, Yan and Qin2013) to reject the irrelevant noise artefact such as ocular artefacts and muscle artefacts. In the end, we applied some filters to remove the unwanted frequency and to maintain meaningful waves for visual recognition and mental imagination.

4.4. Generative model

4.4.1. Training stage one – EEG feature encoder

The objective of this work is to map the stimulated brain signals into the corresponding latent representation of seen images, and thus to build a model to extract EEG features as correlated to the image features as possible.

A recurrent neural network using a LSTM (Hochreiter & Schmidhuber Reference Hochreiter and Schmidhuber1997) cell was employed to track the temporal dynamics in the EEG data which contains fundamental information for EEG activity comprehension. LSTMs are common techniques that have been developed to improve long-term dependency modelling. The brain signal is a long time sequence with very high time dependency, which means the interpretation of the brain activity is not only influenced by the previous 1-ms signal but also influenced by the brain signal long before. Therefore, the LSTM was used to learn a long-term dependency. Figure 5 illustrates the architecture of our EEG feature encoder. This is made up of a standard LSTM layer and two fully connected layers (linear combinations of input, followed by ReLU nonlinearity). At each time step $ t $, the data of all EEG channels at time $ t $ is fed into the LSTM layer; The output of the LSTM layer at the last time step is used as the input of the fully connected layers, ReLU nonlinearity is appended after the first fully connected layer and a Softmax layer is appended after the last fully connected layer. The learning rate is initialized to 0.0001 and gradient descent is used to learn the model’s parameters end-to-end. The dataset is split into three sets: 80% EEG data for training, 10% EEG data for validation, 10% EEG data for testing. Figure 6 illustrates the confusion matrix among five classes, with a total of 1500 EEG data points (300 per class), which includes 1200 data points for training, 150 data points for validation and 150 data points for testing. The overall classification accuracy on the test set which contains five classes is 71.4%. A confusion matrix summarizing the classification results is shown in Figure 6. It was observed that the error for headphone-watch was larger, possibly caused by the similar ‘round and ring shape’ of the two objects. Examples of images that a headphone is misclassified as watch have been illustrated on the right of the confusion matrix.

Figure 5.

EEG feature encoder.

Figure 6.

Confusion matrix for the EEG encoder and examples of misclassified images. The ($ i,j $) element in the confusion matrix represents the frequency product from the $ i $th class, classified as $ j $th class.

4.4.2. Training stage two – generator network

The general view on model architecture is shown in Figure 7. The foundation of the generator framework is ACGAN (Odena et al. Reference Odena, Olah and Shlens2016). This generates images based on the input feature vector and also has the ability to generate images from the specific category. ACGAN consists of a generative model $ G $ and two discriminative models $ {D}_a $ and $ {D}_b $. The generator $ G\left(x|c\right) $ is trained to capture the target data distribution $ {p}_{data}(x) $ from the condition EEG feature $ c $ of class $ y $ and noise distribution $ {p}_z(z) $, and aims to generate images of the target class as real as possible to make the discriminator recognize the generated images are real. Whereas the discriminative model $ {D}_a\left(x|y\right) $ is a binary class classifier which distinguishes whether a sample image belongs to the real image set. The discriminative model $ {D}_b\left(x|y\right) $ is a multiclass classifier that identifies the image class. Both the generative and discriminative models are trained simultaneously and play against each other to minimax the log-likelihood value function V (D, G).

$$ \underset{G}{\min}\underset{D}{\max }V\left(D,G\right)={\unicode{x1D53C}}_{x\in {p}_{\mathrm{data}}(x)}\left[\log {D}_a\left(x|y\right)+\log {D}_b\left(x|y\right)\right]+{\unicode{x1D53C}}_{z\in {p}_z(z)}\left[\log \left(1-{D}_a\left(G\left(x|c\right)|y\right)\right)+\log \left(1-{D}_b\left(G\left(x|c\right)|y\right)\right)\right] $$

Figure 7.

General view on model architecture.

4.4.3. Generator

The generator consists of five upsampling layers. First, inputs of the EEG representation which is the element-wise product of the 64-dimensional EEG features and a random Gaussian noise have been made. The input vector is then spatially upsampled by four times by the first transposed convolutional layers and output 512 feature maps. After that, the number of feature map halves and the feature map size doubles after each remaining transposed convolutional layer. Finally, the final output has been obtained as the 64 × 64-pixel images with three colour channels. Batch normalization (Ioffe & Szegedy Reference Ioffe and Szegedy2015) and LeakyReLU (Maas et al. Reference Maas, Hannun and Ng2013) nonlinearities have been appended after each transposed convolutional layer (Table 1).

Table 1.

Hyperparameters architecture of the generator

4.4.5. Training procedure

To balance the generator and discriminator, we train the generator 10 times per iteration unless the loss of the generator is less than the 10-fold loss of the discriminator. The training procedure for each epoch is shown in Figure 8. We only have 50 EEG correlated images for each class. To avoid the overfitting problem on direct training GAN on a small dataset, we train our GAN model in two stages. In the first stage, we train the GAN with the larger dataset which is gathered manually based on the ImageNet. This dataset contains 10,000 images in total (2000 images per class with a total of five classes), only including images without EEG signals. All conditioned EEG features are set to the average feature value of the class that the image belongs to. In the second stage, we retrain the GAN model for 50 more epochs on the small dataset that contains 50 EEG-available images per class, providing the correct EEG feature.

Figure 8.

Training procedure for each epoch.

4.5. Utilizing stage – generating images with trained models with results verification

Following the method described above, the EEG data collected from the image presentation session were used to train the encoder, and then 10,000 images gathered manually from the ImageNet were used to train the generator. After we obtained a model where both the encoder and generator reached the performance mentioned above, we started to use the model in the design cases. In the model utilizing stage, the data collected from the preference imagery session were input into the model to generate the correlated mental image.

To verify whether this EEG-driven generative method could have a higher chance to capture human preference, a questionnaire survey was conducted in order to provide a proof-of-concept. The control group and intervention group of generated images (with preference EEG/without EEG) were involved in this human study survey. During the survey experiment, 200 generated images were randomly selected from the results generated from our model. Among them, 100 images were selected from the results generated with preference EEG signal, and the other 100 images were selected from these generated without EEG signals. Each set of 100 images contains five classes of images and 20 images per class. Six participants who had been involved in both the image presentation session and preference imagery session evaluated these images. Participants were required to rank the images by preference level 1–10 (10 represents most preferred) from the selected images with 100 images from each group. For each trial of the evaluation experiment, the participants viewed a printed set of generated images and were required to rank the images. After this survey experiment, statistical analysis was performed for each category in two groups. The evaluation results indicated that the design images which are generated by preferred brain signals gained a higher chance to generate a preferred image.

5.2. Visual examination and quantitative study for proof-of-concept

To verify whether the participants preferred the generative design results conditioned on preference brain signal than those without brain signal, both visual examination and quantitative studies were performed. Visual examination was used for checking whether our model has achieved a meaningful quality, that the EEG encoder maintains a good classification accuracy and the image generator reaches the image generation requirement. A quantitative study was performed for comparing whether the score of controlled with preferred EEG ranked higher than the one without EEG from the questionnaire survey. The details of the questionnaire survey are described in the previous Section 4

In the qualitative study, the generated results demonstrated that the proposed approach successfully generates different designs with multiple colour and shape features from different product classes. As mentioned in Section 4.4.1, the overall classification rate of the encoder is 71.4%. To judge the realism and diversity of the produced image, we use the Inception score (Salimans et al. Reference Salimans, Goodfellow, Zaremba, Cheung, Radford and Chen2016) which is commonly used to evaluate the quality of images generated by GANs. An inception model, score measures two things simultaneously. The first concerns whether the images contain meaningful items, indicated by the distribution $ p\left(y|x\right) $ having low entropy. The other is whether the images have variety; the marginal $ \int \left(y|x=G(z)\right) dz $ have high entropy. Therefore, we obtain the final inception score as $ \mathit{\exp}\left({E}_x KL\left(p\left(y|x\right)\Big\Vert p(y)\right)\right) $. An inception score of 4.9 was obtained on the generated images. This is similar to the inception score of 5.1 achieved in the study by Spampinato et al. (Reference Spampinato, Palazzo, Kavasidis, Giordano, Souly and Shah2017) while we have much fewer classes of images for training.

The quantitative study result is from the questionnaire survey, in which all participants were required to rank the image from 1 to 10 based on their preference. Figure 10 shows the mean value and its standard deviation of each category from the two groups of generated images (without EEG signal and with preferred EEG signal). The difference between the two groups is assessed using the Wilcoxon signed-rank test. All group tested had statistically significant differences (p < .05) in their means with the EEG being greater than the non-EEG for all cases, except for the Guitar class (p = .07). Please refer to Figure 10 for more details. The analysis of the two study experiments (qualitative and quantitative) indicates that the images generated with preference EEG signals gained more preference than images generated by the generator itself. Comparing the scores from two controlled groups, the results also show that the generative model with the input of preference EEG signal had a higher chance to generate an image that people preferred. We observed that the reconstructed imagined images have a larger variety of colour and shape features than the reconstructed seen images. The preference imagery experiment results also show that these preferred products generated by the deep learning method through brain activities have combined multiple design features from various kinds of products which learned from previous designs. Also, we take the output from the LSTM layer as the EEG feature is not the final output, as we believe it may contain other features such as the shape or colour or products’ style. Therefore, it may be inferred that these generated designs contain mixed colour and shape features which have been filtered by human cognition by inputting brain signal into a deep generative model.

Figure 10.

Human study results of the design case study.