[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124281-en":3,"doc-seo-124281-105":31,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},124281,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Classification of Music Preferences Using EEG Data in Machine Learning Models - Abstract","Wearable devices with sensors enable continuous biosignal measurement, unlocking new ways for human-computer interaction in music listening. Many recommender systems rely on implicit feedback that may not represent real-time user preferences. This pilot study records EEG while participants listen to music and provide ratings, then trains machine learning models to classify whether each song is liked or disliked using EEG signals from 2s time frames. Results support the feasibility of wearable EEG for capturing individual music preference with real-time capability.","Classification of Music Preferences Using EEG Data in Machine  \nLearning Models  \nHelen Vedder  \nhelen.vedder@t-online.de Karlsruhe Institute of Technology (KIT) Karlsruhe, Germany  \nFabio Stano  \n[fabio.stano@kit.edu](fabio.stano@kit.edu)[ ](fabio.stano@kit.edu)Karlsruhe Institute of Technology (KIT) Karlsruhe, Germany  \nMichael T. Knierim  \n[michael.knierim@kit.edu](michael.knierim@kit.edu)[ ](michael.knierim@kit.edu)Karlsruhe Institute of Technology (KIT) Karlsruhe, Germany  \nAbstract  \nWearable devices equipped with various sensors are increasingly introduced as daily companions in our lives. Especially the continuous measurement of biosignals like heart rate or brain activity offers untapped potential for interaction between humans and computers, including in the domain of music listening. Current music recommendation systems often rely on implicit feedback, which may not accurately reflect a user’s true and current preferences. Biosignal sensors can provide real-time, objective data on listeners’ reactions to music, eventually allowing for a more accurate and emotionally responsive music experience. In this pilot study, participants listened to music and gave their ratings while brain activity was simultaneously measured using EEG. Machine learning models were trained to predict whether a song would be liked or disliked based on the brain activity related to 2s time frames of music listening. The project offers first insights into the feasibility of wearable EEG for capturing users’ individual music preferences with real-time capability.  \nCCS Concepts  \n• Applied computing → Sound and music computing; • Computing methodologies → Machine learning.  \nKeywords  \nEEG, BCI, Music Preference, Classification, EEGNet  \n1 Introduction  \nMany mobile devices today are equipped with sensors capable of detecting a wide range of biosignals. One doesn’t need to look as far as Elon Musk’s company, Neuralink, which recently received approval to conduct the first tests of its implantable brain-computer interfaces on humans in the US [25], or Apple, which has filed an first patent for in-ear electroencephalogram (EEG) headphones [24] . Also scientific work in this area is constantly expanding with innovations such as cEEGrids [3], flexible electrode stickers for the ear, or 3D-printed headphones with electrodes around the ear and in the holder [3, 11]. These latest developments mark the beginning  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s) .  \nMensch und Computer 2024 – Workshopband, Gesellschaft für Informatik e.V., 01.-04. September 2024, Karlsruhe, Germany  \n© 2024 Copyright held by the owner/author(s) . Publication rights licensed to GI.  \n[https://doi.org/10.18420/muc2024-mci-src-324](https://doi.org/10.18420/muc2024-mci-src-324)  \nof an exciting future in which headphones with integrated sensors could play an important role. These sensors could collect real-time EEG data to adapt the music experience to personal preference or to suit the situation. Such an advance could be particularly beneficial for people with physical disabilities, as it would enable them to interact with music players in a way that doesn’t require active control. Also in situations, where other interaction modalities are not applicable, a recommender system that could understand the user’s enjoyment and automatically react to that information could be useful. Context-aware recommender systems (CARS) [1] have gained significant traction by leveraging contextual information, such as location, time, and user activity, to provide more relevant recommendations. However, an underused form of context is human physiology ","cbCaiqvlhn3OlEzS","https://ap.wps.com/l/cbCaiqvlhn3OlEzS","pdf",1047589,2,1,4,"English","en",105,"# Abstract\n# Introduction\n# Theoretical Background and Related Work","[{\"question\":\"What is the goal of this pilot study?\",\"answer\":\"To test whether wearable EEG signals can capture individual music preferences on a single-trial basis and enable real-time classification of liked versus disliked songs.\"},{\"question\":\"How is the EEG data used in the machine learning models?\",\"answer\":\"EEG measurements are collected while participants listen to music, and models are trained to predict like/dislike from EEG activity in 2-second time frames.\"},{\"question\":\"Why does the study emphasize wearable sensors and biosignals?\",\"answer\":\"Biosignal sensors provide objective, real-time information about listeners’ reactions, potentially overcoming the limitations of recommender systems that depend on implicit feedback.\"}]","Classification of Music Preferences Using EEG Data in Machine Learning Models - 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