How does the human brain decide someone is beautiful? It is something universal or subjective? This AI attempts to help give us some answers to these questions. This AI reads your brain to generate personally attractive faces. It generates images containing optimal values for personal attractive features on a human face! Watch the video References DeepFakes in 5 minutes: GANs explained: An amazing application of GANs: Read the article: M. Spape, K. Davis, L. Kangassalo, N. Ravaja, Z. Sovijarvi-Spape and T. Ruotsalo, "Brain-computer interface for generating personally attractive images," in IEEE Transactions on Affective Computing, doi: 10.1109/TAFFC.2021.3059043. https://youtu.be/Upv0kMLx7xI https://youtu.be/ZnpZsiy_p2M https://youtu.be/zB_jQ8SUjKE https://whats-ai.medium.com/ Abstract— While we instantaneously recognize a face as attractive, it is much harder to explain what exactly defines personal attraction. This suggests that attraction depends on implicit processing of complex, culturally and individually defined features. Generative adversarial neural networks (GANs), which learn to mimic complex data distributions, can potentially model subjective preferences unconstrained by pre-defined model parameterization. Here, we present generative brain-computer interfaces (GBCI), coupling GANs with brain-computer interfaces. GBCI first presents a selection of images and captures personalized attractiveness reactions toward the images via electroencephalography. These reactions are then used to control a GAN model, finding a representation that matches the features constituting an attractive image for an individual. We conducted an experiment (N=30) to validate GBCI using a face-generating GAN and producing images that are hypothesized to be individually attractive. In double-blind evaluation of the GBCI-produced images against matched controls, we found GBCI yielded highly accurate results. Thus, the use of EEG responses to control a GAN presents a valid tool for interactive information-generation. Furthermore, the GBCI-derived images visually replicated known effects from social neuroscience, suggesting that the individually responsive, generative nature of GBCI provides a powerful, new tool in mapping individual differences and visualizing cognitive-affective processing. Video Transcript: 00:00 i think you will all agree that when you 00:02 look at someone 00:03 and find the person attractive you 00:05 cannot explain why 00:07 there are many reasons involved in this 00:09 decision 00:10 plus some of these reasons may not even 00:12 be directly related to the physique of 00:14 the person 00:15 still when you look at a picture of 00:17 someone you instantly know if the person 00:19 is attractive or not by your standards 00:21 and you know this without any doubts but 00:24 how does that happen 00:26 can we explain or understand what beauty 00:28 is to us and worse can someone else 00:31 understand what we find beautiful is 00:34 beauty even something universal 00:36 or subjective to me it's definitely 00:39 something subjective 00:40 just like art as we say beauty is in the 00:43 eye of the beholder 00:44 isn't it but then why specific people 00:47 become 00:48 actors or models and not others is there 00:51 a universal beauty that we could explain 00:53 is beauty something cultural biological 00:56 or a blend of both 00:58 i think these are mostly philosophical 01:00 debates that we can discuss in the 01:01 comments 01:02 for those of you that will like to i 01:04 would love to chat and even debate about 01:06 this with you 01:07 anyways back to what we know about 01:09 beauty as we know 01:10 humans typically respond emotionally to 01:13 attractive images 01:14 rather than purely based on rational 01:16 visually salient reasons 01:18 which is what makes beauty so subjective 01:20 but is there a way to understand what we 01:22 think 01:23 of beauty and use this information to 01:25 create attractive pictures 01:27 can we understand our brain signals to 01:29 achieve that 01:30 and is it even readable to these signals 01:33 well 01:34 this is what michel spape and his team 01:36 from the university of helsinki 01:38 attempted to do what you see here is 01:41 called electroencephalography 01:43 or eeg it's a monitoring method used to 01:46 record electrical brain activity 01:48 using electrodes placed along the scalp 01:51 specifically they tracked 01:52 two different channels with these 01:54 electrodes which are known to be 01:56 responsive to stimuli that are either 01:58 novel or effectively evocative or 02:01 mentally demanding and related to 02:03 working memory updating 02:05 meaning that the stimuli are improbable 02:07 and require a mental or physical 02:09 response 02:10 but they employed two strategies in 02:11 their experiments 02:13 first they made the supposed attractive 02:15 images relatively improbable to appear 02:18 they also asked the participants to 02:20 focus on attractive faces 02:21 by mentally counting their occurrence to 02:24 me these strategies seem like a 02:26 considerable bias to detecting 02:28 actual attractiveness where the signal 02:30 responses mainly tell us two things 02:33 one that the person is lightly surprised 02:35 because the attractive pictures are rare 02:37 and two that the person is mentally 02:39 active trying to remember the count and 02:41 add to it 02:42 i personally see it as an overkill way 02:45 of asking whether the person finds the 02:46 face attractive or not 02:48 since it does not really measure the 02:50 beauty of the image scene 02:52 and here i am not entering about the 02:54 details of the data set they used to 02:56 train their model 02:57 which is only made of celebrities 02:59 nonetheless 03:00 this was undoubtedly a great way to find 03:03 out 03:03 which faces were attractive or not to 03:06 train their model to focus on beautiful 03:08 faces 03:08 which is a very cool application of guns 03:11 as you can see it clearly worked out 03:13 there's a significant difference between 03:15 responses after a slight delay of time 03:17 starting approximately a fourth of a 03:19 second after showing the image 03:21 where the pink lines are the two channel 03:24 responses i just mentioned 03:25 are shown here the top shows the frontal 03:28 response 03:29 responsible for the novel and 03:31 effectively evocative stimuli while the 03:33 bottom is the parietal response 03:35 responsible for mentally demanding 03:37 stimuli here 03:38 the faces deemed attractive are green 03:40 unattractive in red 03:42 and inconsistent responses in gray these 03:45 inconsistent responses signified that 03:47 the data 03:48 was not used due to its low level of 03:50 confidence 03:51 attractiveness was confident when both 03:53 electrodes evoked more positivity for 03:55 the attractive faces 03:57 now let's see how they created such a 03:59 model to use their brain activity to 04:01 generate attractive faces 04:03 at first they needed to generate random 04:05 images 04:06 to show the test subjects to do that 04:08 they used a gun model trained on 200 000 04:11 images of celebrity faces already 04:14 introducing a bias 04:15 but still let's keep going they then had 04:18 a latent space 04:19 from which they could use to generate 04:21 new artificial images 04:22 this is just like any other gun model 04:25 like deep fakes 04:26 it is very similar to what i already 04:28 explained in previous videos 04:29 and you are most certainly aware of how 04:31 it works so i won't cover it again here 04:34 but if you are not 04:35 you are free to click on the top right 04:37 pop-up and watch my explanations of 04:39 gan architectures and i'll talk a little 04:41 bit more about this latent space 04:43 later on then each participant was asked 04:47 to go through 04:47 randomly generated images remember here 04:51 they had to count when they see an 04:52 attractive face mentally 04:54 once the results are all compiled they 04:56 train a classifier on these brain output 04:59 signals 04:59 containing the image's subject 05:01 assessments 05:02 basically ideally containing whether the 05:05 face was attractive or not 05:07 once the classifier is trained it's used 05:09 under brain electrical responses 05:11 on randomly generated images note that 05:14 all these randomly generated images come 05:17 from a latent space originally 05:19 created by the encoder of the gun 05:21 trained on the first step 05:22 as you can see here the latent space is 05:25 a space where one point represents 05:27 an input the generator can use to create 05:29 a unique image 05:30 this space contains all the faces that 05:33 this generator can make 05:35 thus when an image is generated using a 05:37 point in the space 05:38 or feature vector it can be considered 05:41 attractive or not 05:42 by the classifier if that's the case it 05:44 will encourage the network to create 05:46 images by selecting the generator's 05:48 input close to this point in the space 05:50 focusing the generator's attention on a 05:52 subspace 05:53 where the features of the generated 05:55 faces are pleasing to the participant 05:58 finally this is how new images 06:00 containing optimal values for personal 06:02 attractive features on a human face 06:05 are generated i hope you enjoyed this 06:07 video 06:08 and i definitely invite you to read 06:09 their paper it's very interesting and 06:12 well written it's the first link in the 06:14 description don't forget to like and 06:15 subscribe if you enjoyed the video 06:17 and share your thoughts in the comments 06:19 where i will answer you in the following 06:21 7 minutes 06:22 i promise thank you for watching
