{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/decoding-generic-visual-representations-from","title":"Decoding Generic Visual Representations From Human Brain Activity using Machine Learning","arxiv_id":"1811.01757","date":"2018-11-05","proceeding":null,"authors":["Angeliki Papadimitriou","Nikolaos Passalis","Anastasios Tefas"],"abstract":"Among the most impressive recent applications of neural decoding is the\nvisual representation decoding, where the category of an object that a subject\neither sees or imagines is inferred by observing his/her brain activity. Even\nthough there is an increasing interest in the aforementioned visual\nrepresentation decoding task, there is no extensive study of the effect of\nusing different machine learning models on the decoding accuracy. In this paper\nwe provide an extensive evaluation of several machine learning models, along\nwith different similarity metrics, for the aforementioned task, drawing many\ninteresting conclusions. That way, this paper a) paves the way for developing\nmore advanced and accurate methods and b) provides an extensive and easily\nreproducible baseline for the aforementioned decoding task.","url_abs":"http://arxiv.org/abs/1811.01757v1","url_pdf":"http://arxiv.org/pdf/1811.01757v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"decoding-generic-visual-representations-from","repo_url":"https://github.com/angpapadi/Visual-Representation-Decoding-from-Human-Brain-Activity","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}