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OPINION article

Front. Aging Neurosci.
Sec. Neurocognitive Aging and Behavior
Volume 16 - 2024 | doi: 10.3389/fnagi.2024.1498821
This article is part of the Research Topic Progress in the Assessment and Intervention of Neurocognitive Aging and Neurodegenerative Diseases View all 6 articles

Integrating Music Therapy and Video Games in Cognitive Interventions: Innovative Applications of Closed-Loop EEG

Provisionally accepted
Ying Wang Ying Wang 1Kexin Zhang Kexin Zhang 1Hao Yu Hao Yu 2Xianglong Wan Xianglong Wan 2Tiange Liu Tiange Liu 2Danyang Li Danyang Li 2Dingna Duan Dingna Duan 2Xueguang Xie Xueguang Xie 2Dong Wen Dong Wen 2*
  • 1 Yanshan University, Qinhuangdao, Hebei, China
  • 2 University of Science and Technology Beijing, Beijing, China

The final, formatted version of the article will be published soon.

    Cognitive function refers to the mental abilities involved in acquiring, manipulating, and applying information, including but not limited to attention, memory, reasoning, language, spatial and executive functions (Ni et al., 2022). These cognitive abilities can deteriorate with age or due to certain medical conditions, leading to symptoms like memory loss, slowed thinking, and concentration difficulties (Unbehaun et al., 2021). Cognitive impairment often serves as an early indicator of diseases like Alzheimer's and Parkinson's disease (PD) (Litvan et al., 2011). Therapeutic interventions like video games (VGs) and music therapy (MT) have demonstrated effectiveness in preventing or mitigating cognitive decline and enhancing cognitive function. VGs, known for their engaging and action-oriented nature, can foster improvements in cognitive skills through social and participatory elements (Wiley et al., 2021). In contrast, MT utilizes music to stimulate neuroplasticity, thereby enhancing cognitive function (Fang et al., 2017). The combination of MT and VGs provides a more robust therapeutic intervention than either approach alone, yielding a synergistic effect with substantial therapeutic potential (Martin-Moratinos et al., 2023). Music video games (MVGs) have also shown promise in preventing or mitigating cognitive decline and enhancing cognitive function. In recent years, cognitive interventions based on electroencephalography (EEG) have gained considerable attention in psychology and neuroscience. These interventions utilize EEG to monitor brain activity, assess the effects of cognitive training (Rajakumar and Mohan, 2024), and guide the optimization of therapeutic strategies (Taya et al., 2015). Traditional open-loop EEG systems primarily record and analyze brain activity but lack a time feedback mechanism (Jin et al., 2024). In contrast, closed-loop EEG can monitor brain states in real time (Zhang et al., 2023), allowing for timely adjustments to intervention strategies and providing a more personalized and practical cognitive training experience (Dangi et al., 2013). As a result, closed-loop EEG-based music video game therapy (MVGT) shows excellent clinical potential in cognitive interventions. This paper summarizes and analyzes cognitive intervention methods that combine MT and VGs, including the application of EEG in assessing intervention effectiveness. We explore EEG analysis techniques and present our perspective on the developmental trends of closed-loop EEG-based MVGT in cognitive interventions, aiming to offer valuable insights for future research.

    Keywords: Music Therapy, Video Games, Cognitive Function, EEG, combination

    Received: 19 Sep 2024; Accepted: 29 Nov 2024.

    Copyright: © 2024 Wang, Zhang, Yu, Wan, Liu, Li, Duan, Xie and Wen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Dong Wen, University of Science and Technology Beijing, Beijing, China

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