[VIE Co., Ltd.] VIE Announces Research Results to Enhance Nostalgia, Memory Vividness, and Well-Being by Listening to Playlists Created Using EEG and AI
VIE Inc. Press Release: September 9, 2025 VIE Announces Research Results Showing that Listening to Playlists Created Using EEG and AI Enhances Nostalgia, Memory Vividness, and Well-Being The results of this research have been implemented in “Uta Memory,” a product for active seniors that uses nostalgic melodies to assist with memory recall. VIE Inc. (Headquarters: Kamakura, Kanagawa Prefecture; CEO: Yasuhiko Imamura; hereinafter referred to as VIE), a company developing next-generation wearable electroencephalographs and implementing neurotechnology in society, has published the results of its research on the Nostalgia Brain-Music Interface (N-BMI) in Scientific Reports. N-BMI uses brainwaves and AI to recommend music, evoking feelings of nostalgia and contributing to improved memory recall and well-being. In this study, we used VIE’s wearable electroencephalography (EEG) to develop a predictive model that combines brain activity during music listening and subjective nostalgia ratings. We then constructed the “Nostalgia Brain-Music Interface (N-BMI),” a system that automatically recommends
“nostalgia-evoking music” optimized for each individual. Experimental results showed that listening to music recommended by N-BMI
significantly improved nostalgia feelings, state-level well-being, and subjective memory vividness in both young and older adults. Image
URL: https://prcdn.freetls.fastly.net/release_image/67474/75/67474-75-3cbf5b4f0cc3bab32987baf299ce9776-1920×1005.png
environmental science, and has an impact factor of 4.6 as of 2025. Background of the Research The world’s population is rapidly aging, with those aged 60 or over projected to reach approximately 22% by 2050 and approximately 32% by 2100*1. Given this, a major challenge is how to extend “the amount of time spent feeling healthy both physically and mentally = healthy lifespan.” Previous research has shown that feelings of nostalgia can reduce feelings of loneliness*2, increase self-esteem and optimism*3, and vividly recall past events*4. Therefore, the experience of nostalgia is thought to potentially contribute to healthy lifespan. Music, in particular, is a powerful factor in eliciting nostalgia, and has been shown to be associated with stress reduction and long-term memory retention.*5 However, because the music that evokes nostalgia varies greatly from person to person, it is difficult to identify in advance which songs will be effective for each individual, posing a hurdle when actually using this technology in nursing care and clinical settings. With this background in mind, VIE has developed a system that combines EEG and music feature analysis to automatically recommend “nostalgia-evoking songs” that are optimal for each individual using EEG and machine learning. In the experiment, young and elderly subjects listened to nostalgic music they selected themselves (self-selected songs) and music selected by others (other-selected songs), and their EEG and subjective nostalgia ratings were recorded simultaneously. Based on this data, we constructed a model to predict nostalgia and a model to classify whether brainwave patterns are eliciting nostalgia. We then developed the “Nostalgia Brain-Music Interface (N-BMI)” algorithm, which recommends optimal music from a database of thousands of songs. *1: Lutz, W., Sanderson, W. & Scherbov, S. The coming acceleration of global population aging. Nature 451, 716-719 (2008). *2: Zhou, X. et al. The Restorative Power of Nostalgia: Thwarting Loneliness by Raising Happiness During the COVID-19 Pandemic. Soc. Psychol. Person. Sci. 13, 803-815 (2021). *3: Jiang, T., Cheung, W. Y., Wildschut, T. & Sedikides, C. Nostalgia, reflection, brooding: Psychological benefits and autobiographical memory functions. Conscious. Cogn. 90, 103107 (2021). *4: Ismail, S. et al. Psychological and Mnemonic Benefits of Nostalgia for People with Dementia. J. Alzheimers Dis. 65, 1327-1344 (2018). *5: Sedikides, C., Leunissen, J. & Wildschut, T. The psychological benefits of music-evoked nostalgia. Psychol. Music. 50, 2044-2062 (2022). Research results ◆Subjective evaluation after listening to self-selected songs/songs selected by others In the study, young participants were asked to select three songs that they found nostalgic (self-selected nostalgia songs). For comparison, they were also asked to listen to songs selected by another participant of the same generation (other-selected nostalgia songs). The elderly group were asked to select three songs that they felt nostalgic for (self-selected nostalgia songs) from a list of songs that were popular during their adolescence (around age 15), and for comparison, they were also asked to listen to songs selected by the younger group (other-selected nostalgia songs). After listening to each song, participants were asked to rate “nostalgia,” “momentary well-being,” and “vividness of memory” using a VAS. In the younger group, ratings after listening to self-selected nostalgia songs were significantly higher than those for songs selected by others, with increases in nostalgia feelings, state-level well-being, and subjective memory vividness [Figure 1-A]. A similar trend was observed in the older group, with self-selected songs showing significantly higher scores [Figure 1-B].
https://prcdn.freetls.fastly.net/release_image/67474/75/67474-75-aeab8b76830295804918b1f2660540b8-685×255.jpg [Figure 1-A,B] Subjective evaluation after listening to songs selected by myself/others ◆Model Accuracy (Model 1/Model 2) Model 1 (LASSO regression), which predicts nostalgia from song characteristics, was successfully trained with high accuracy. Model 2 (Nostalgia Decoder), which distinguishes between self-selected and third-party nostalgia songs based on EEG patterns during music listening, showed an average accuracy of approximately 64% for the younger group and approximately 72% for the older group (Mean ± SD: Young 63.97 ± 16.11%, Elderly 71.52 ± 19.88%). Both results were significantly above the chance level of 50%, confirming that nostalgia states can be interpreted from EEG with higher accuracy, especially in the older group. ◆Decoding indicators during feedback Using Model 1 and Model 2 described above, we created an algorithm that automatically and optimally selects “songs that enhance nostalgia” and “songs that do not evoke nostalgia” from a database of several thousand songs. This algorithm then presented a playlist in which a song was selected every 20 seconds, totaling six songs (120 seconds long). The nostalgia index calculated using a wearable electroencephalograph tended to increase in the younger group when listening to a “nostalgia-enhancing playlist,” but the difference was not significant [Figure 2-A]. On the other hand, the nostalgia index in the older group increased significantly when listening to “nostalgia-enhancing songs” [Figure 2-B], confirming that the brainwave patterns approached those observed when listening to self-selected songs.
https://prcdn.freetls.fastly.net/release_image/67474/75/67474-75-ac5b5ac5dc8b362fba84c87526f66970-685×424.jpg [Figure 2-A,B] Decoding indicators during feedback ◆Subjective evaluation during feedback In the younger group, the condition in which songs that enhanced nostalgia were presented (nostalgic condition) significantly increased nostalgic feelings and state-level well-being compared to the condition in which songs that did not evoke nostalgia were presented (non-nostalgic condition). However, the difference in subjective memory vividness did not reach significance, but only tended to be significant [Figure 3-A]. In the elderly group, nostalgia emotion, state-level well-being, and subjective memory vividness all showed significantly higher values in the nostalgic condition [Figure 3-B]. These results indicate that while music recommendation based on N-BMI is effective for both generations, the effect is particularly pronounced in elderly people.
https://prcdn.freetls.fastly.net/release_image/67474/75/67474-75-0900d974e4c9045bcb39838efdbfaedd-685×260.jpg [Figure 3-A,B] Subjective evaluation during feedback In this study, we developed the “Nostalgia Brain-Music” model, which combines EEG and the acoustic features of music. The N-BMI (Nursing Body Mass Index) Interface (N-BMI) was shown to enhance nostalgia, state-level well-being, and subjective memory recall clarity in both younger and older adults. In particular, the effects were confirmed in the older adult group based on both EEG data and subjective assessments. These results demonstrate the effectiveness of a music recommendation system using nostalgia as an index. Further validation is needed to explore its potential as a non-pharmacological approach, such as for dementia prevention and caregiving. For details on the N-BMI algorithm and a discussion of differences in effectiveness between age groups, please refer to this paper. Paper information: Yuna Sakakibara, Tomohiro Kusutomi, Sotaro Kondoh, Takahide Etani, Saori Shimada, Yasuhiko Imamura, Yasushi Naruse, Shinya Fujii & Takuya Ibaraki / A Nostalgia Brain-Music Interface for enhancing nostalgia, well-being, and memory vividness in younger and older individuals Implementation of research results
https://prcdn.freetls.fastly.net/release_image/67474/75/67474-75-36aae5f26e14016c6a9c2bb9c9fa60f8-1920×1005.jpg The “Nostalgia Brain-Music Interface (N-BMI)” developed in this research is not only academically significant, but is also being applied to actual services. The research results have been implemented in “Uta Memory,” a product for active seniors that supports memory recall through nostalgic music. It is being used as a system to support the psychological well-being and memory of seniors in their daily lives. Product HP: https://uta-memory.com/ Recruiting joint research partners VIE Inc. pursues new possibilities by combining cutting-edge neurotechnology. We welcome collaborative research across a wide range of phases and themes, from research planning to joint research and development, service development, and business
implementation. Please feel free to contact us. ・Contact:
info@vie.style ・Joint research example:
https://www.viestyle.co.jp/news/category/business/ VIE Inc. VIE Inc.’s mission is “Feel the life,” and it aims to create a society rich in emotion by combining the power of neurotechnology and entertainment. Through collaborations with pharmaceutical companies, university research institutes, and businesses, we have promoted the development of services utilizing wearable electroencephalographs and
neurotechnology. In particular, we have developed technology that allows for easy measurement of EEG (electroencephalograms) in everyday life, and are developing products and technologies that support the visualization of emotions. We also offer the desktop EEG analysis app “VIE Streamer,” which allows access to EEG data acquired in real time. This app is widely used in research and development departments, university research institutes, hospitals, and other organizations. In March 2024, we raised 305 million yen in a Series A1 round from pharmaceutical companies and other business entities, and are working to further advance research and development and business development. Going forward, we will continue to further promote the spread of neurotechnology and its contributions to well-being and the medical field. ・Company Name: VIE Co., Ltd. ・Representative Director: Yasuhiko Imamura ・Location: 1-9-22 Omachi, Kamakura City, Kanagawa Prefecture ・URL: https://www.viestyle.co.jp/ Contact information regarding this matter
https://prcdn.freetls.fastly.net/release_image/67474/75/67474-75-7652ddebb5431a2b84ad9d0654e5d4ca-1073×1350.jpg E-mail: info@vie.style For more information about this release
