{"id":114513,"date":"2020-10-16T03:29:15","date_gmt":"2020-10-16T10:29:15","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2020\/10\/neuroadaptive-modelling-for-generating-images-matching-perceptual-categories"},"modified":"2020-10-16T03:29:15","modified_gmt":"2020-10-16T10:29:15","slug":"neuroadaptive-modelling-for-generating-images-matching-perceptual-categories","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2020\/10\/neuroadaptive-modelling-for-generating-images-matching-perceptual-categories","title":{"rendered":"Neuroadaptive modelling for generating images matching perceptual categories"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/neuroadaptive-modelling-for-generating-images-matching-perceptual-categories.jpg\"><\/a><\/p>\n<p>Brain\u2013computer interfaces enable active communication and execution of a pre-defined set of commands, such as typing a letter or moving a cursor. However, they have thus far not been able to infer more complex intentions or adapt more complex output based on brain signals. Here, we present <i>neuroadaptive generative modelling<\/i>, which uses a participant\u2019s brain signals as feedback to adapt a boundless generative model and generate new information matching the participant\u2019s intentions. We report an experiment validating the paradigm in generating images of human faces. In the experiment, participants were asked to specifically focus on perceptual categories, such as old or young people, while being presented with computer-generated, photorealistic faces with varying visual features. Their EEG signals associated with the images were then used as a feedback signal to update a model of the user\u2019s intentions, from which new images were generated using a generative adversarial network. A double-blind follow-up with the participant evaluating the output shows that neuroadaptive modelling can be utilised to produce images matching the perceptual category features. The approach demonstrates brain-based creative augmentation between computers and humans for producing new information matching the human operator\u2019s perceptual categories.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Brain\u2013computer interfaces enable active communication and execution of a pre-defined set of commands, such as typing a letter or moving a cursor. However, they have thus far not been able to infer more complex intentions or adapt more complex output based on brain signals. Here, we present neuroadaptive generative modelling, which uses a participant\u2019s brain [\u2026]<\/p>\n","protected":false},"author":427,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1523,47],"tags":[],"class_list":["post-114513","post","type-post","status-publish","format-standard","hentry","category-computing","category-neuroscience"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/114513","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/427"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=114513"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/114513\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=114513"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=114513"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=114513"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}