MT stimulates various areas of the brain and promotes neural plasticity (Stegemoller, 2014). Active MT involves participants enhancing neural connections through learning instruments or engaging in music activities (Bégel et al., 2017). In contrast, passive MT activates sensory, motor, cognitive, language, and emotional regions in both hemispheres of the brain while participants listen to music performed by others (Moreira et al., 2023).

MT is an effective cognitive intervention that not only improves cognitive function (Xue et al., 2023) but also significantly reduces anxiety and depression (Lyu et al., 2018), enhancing overall quality of life. Factors such as music, rhythm, and task difficulty influence its effects (Schwartz et al., 2017). MT has been shown to benefits various patient populations, including those with dementia (Moreno-Morales et al., 2020), attention deficit hyperactivity disorder (Martin-Moratinos et al., 2023), and PD (Barnish and Barran, 2020), effectively enhancing their cognitive abilities.

However, despite its advantages, MT typically requires guidance from a professional music therapist (Li et al., 2015). Participants often need a therapist to implement effective MT independently (Li et al., 2019), which limits their ability to apply MT into daily life.

A comprehensive 68-year study has shown that moderate participation in VGs between the ages of 11 and 79 can enhance cognitive abilities (Altschul and Deary, 2020). The research found that video game training significantly improves cognitive function in older adults, particularly in visual-spatial working memory and episodic memory. Additionally, video game therapy has shown positive outcomes in patients, markedly enhancing overall cognitive function in individuals with schizophrenia (Shi et al., 2023) and improving executive function and information processing speed in those with mild cognitive impairment (Menascu et al., 2021).

However, during cognitive interventions involving gaming, while players may experience improvements in specific cognitive skills, they may also experience negative emotional outcomes (Miller and Mandryk, 2016). Prolonged gaming can result in fatigue, anxiety, or frustration (Kuperczko et al., 2022), especially when confronting challenging tasks. If these emotional issues are not addressed, they may adversely affect the players' overall experience and mental health.

Integrating music with VGs through MVGT presents a promising avenue for cognitive intervention. Music is closely linked to emotions, motivation, and reward within the gaming context (Loizou et al., 2014). By designing music-based VGs, players can enjoy an engaging experience while benefiting from the therapeutic qualities of music (Ticker, 2017). Research indicates that MVGs, which combines music as a motivating factor with gameplay mechanics, can effectively improve memory and spatial awareness in patients with cognitive impairments (Vargas et al., 2020), making them suitable for long-term training.

Moreover, MVGs also facilitate coordination between auditory and motor skills. For instance, rhythm games can be utilized in rehabilitation training for patients with PD (Dalla Bella, 2022), helping to improve their motor abilities. Music also aids participants in concentrating better and focusing on specific game tasks. In a music attention training game, users must follow rhythm and speed, following the music to perform and improvise (Chalkiadakis, 2022). Research indicates that this type of game significantly enhances participants' selective attention, underscoring the considerable potential of MVGs in cognitive intervention.

Overall, by combining the benefits of music therapy and video games, MVGs represent a promising approach to enhancing cognitive abilities in participants. Despite their significant potential, MVGT faces important limitations that must be addressed. For instance, personalized treatment plans and standardized procedures for MVGs still need to be fully developed. Additionally, variations in players' musical and gaming preferences and learning styles may affect the effectiveness of interventions (Liu et al., 2016). Therefore, it is essential to develop innovative assessment and guidance strategies to optimize the implementation process of MVGs.

EEG signals exhibit high variability and non-stationarity across subjects (Jimenez-Guarneros and Gomez-Gil, 2020), which can lead to mismatched data distributions among samples. Current research primarily focuses on single tasks, which limits the generalization capability of EEG analysis models to other tasks within the same cognitive domain (Zhong et al., 2024). Therefore, cross-subject and cross-task EEG signal analysis is essential for effectively assessing the impact of cognitive interventions. The cross-subject analysis utilizes data from other individuals to calibrate a new subject (Wu et al., 2022). In contrast, cross-task analysis employs labeled data from similar or related tasks to calibrate new tasks.

To evaluate the effects of EEG-based cognitive interventions more effectively, it is crucial to establish a general EEG analysis model applicable across different subjects and tasks within the same cognitive domain (Zhou et al., 2022). By integrating closed-loop EEG technology, EEG signals can be analyzed more accurately across different subjects and tasks (Walter, 2015). Recording the EEG patterns of subjects during specific cognitive tasks and comparing the signals of different subjects performing the same task can help identify critical factors influencing cognitive performance (Jimenez-Guarneros and Gomez-Gil, 2020). Additionally, analyzing EEG signals from different cognitive tasks helps reveal the dynamic changes in how the brain processes various types of information (Wen et al., 2023). This approach enhances data collection flexibility and provides essential support for personalized cognitive intervention programs.

The individualized intervention plan is designed to adjust the selection of music and VGs based on each patient's cognitive needs and abilities (Cicerone et al., 2019). Different music and game content types may have varying effects on each participant (Choi et al., 2020), making a tailored approach significantly more effective for cognitive intervention (Buzzi et al., 2019). In this process, closed-loop EEG technology plays a crucial role by allowing real-time monitoring and analysis of each participant's brain signals, providing precise feedback for personalized interventions (Carè et al., 2024). This method enhances patient engagement and satisfaction and facilitates the smooth treatment progress. By utilizing closed-loop EEG, we can more effectively identify individual differences, optimize intervention plans, and improve the effectiveness and sustainability of the interventions.

Future research should focus on the effectiveness of integrated intervention therapies (Jung et al., 2020) to explore their impact on cognitive function. Studies have shown that combining music video games with cognitive training can significantly enhance short-term memory, attention, and inhibitory control in older adults (Lin et al., 2020). Furthermore, integrating music games with exercise therapy has been found to promote social interaction, engagement, and memory recall among dementia patients with dementia (Unbehaun et al., 2021). Therefore, it is recommended that future studies conduct further investigations into integrated cognitive interventions to assess their potential to enhance cognitive function systematically. Comprehensive cognitive intervention therapy will help us understand the impact of different cognitive intervention therapies on cognitive training to optimize the intervention program and promote improving cognitive health.