The human stress recognition of brain, using music therapy

Chavan, Deepika R.; Kumbhar, Mahesh; Chavan, Rohit R.

doi:10.1109/iccpeic.2016.7557197

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…Through various brain wave observations, people can relieve stress by listening to music. The initial EEG signals were divided according to their frequencies, and their amplitudes ranged from 0.5 Hz to 30 Hz [9]. Using this method, the changes of brainwave after intervention were tested and analyzed.…”

Section: Music Software and Implementation Based On Brainwavementioning

confidence: 99%

“…It then explores the software applications used to produce brainwave-enhanced music, providing examples and insights into how they work. The framework then goes into several applications, including stress relief, cognitive improvement, and therapeutic usage, all of which are supported by research and case studies [12]. It does, however, recognize some drawbacks, such as individual variation and ethical issues.…”

Section: Motivationmentioning

confidence: 99%

“…The diversity of musical works reflects the diversity of cultures. A broader range in music creation has progressively emerged alongside the development of computer music production software, reflecting the reality that music may be real or fake, ethnic classical, or pop mainstream [12].…”

Section: Basic Algorithm and Implementationmentioning

confidence: 99%

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Music Production Based on Brainwave and Acoustic Theory

Zhang

2024

HSET

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The fusion of music production and cognitive science has carved a transformative path in an era characterized by tremendous technical advancement and an ever-deepening understanding of the human brain. This essay investigates this ground-breaking nexus in an effort to unravel the complex interplay between brainwave patterns and acoustic components in music and its potential to radically alter musical creativity, production, and emotional resonance. The neurological underpinnings of music's psychological effects have been revealed by recent studies looking into the relationships between brainwave patterns and musical structures. Composers and producers can create songs that are artistically compelling and scientifically informed, capable of evoking desired emotional and cognitive reactions in listeners, by merging brainwave data with acoustic theory. This method also has the potential to be therapeutic, improving emotional wellbeing through cognitive rehabilitation and stress reduction. This research aims to broaden the understanding of the art of music and increase its ability to engage people on deeper cognitive and emotional levels as technology and methodology develop. Beyond the domain of traditional rhythms and notes, it explores the world of brainwaves, enhancing knowledge of the human condition through the transforming power of music.

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Section: Music Software and Implementation Based On Brainwavementioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

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Music Production Based on Brainwave and Acoustic Theory

Zhang

2024

HSET

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“…Most of these works sought to better understand the interventions of music and auditory stimuli in human brain waves (Qin et al, 2020;Dutta et al, 2020;Bhargava et al, 2020;Liao et al, 2017;Q. Li et al, 2020;Soysal et al, 2020;Ramdinwawii & Mittal, 2017;Rushambwa & Mythili, 2017;Chavan et al, 2016;O'Toole et al, 2019;Santhosh et al, 2020;Geethanjali et al, 2019;Liu et al, 2020;Jagiello et al, 2019;Ibrahim et al, 2019). Other works have studied ways in which factors such as musical preference can affect human physiology, mainly through the measurement of hemodynamic aspects (Yamada & Ono, 2019;Huang & Benjamin Knapp, 2017).…”

Section: Physiological and Behavioral Responses Induced By Acoustic Signalsmentioning

confidence: 99%

“…Several studies also acted in the investigation of behavioral changes favored by sound stimuli. In this sense, some works have successfully explored the effects of music in reducing anxiety, stress, and depression (Dragulin et al, 2019;Lin et al, 2020;˘ Wang et al, 2016;Mitrpanont et al, 2017;Chavan et al, 2016;Chennafi et al, 2018;Liu et al, 2020;Geethanjali et al, 2019). Other groups studied the effects of these sound stimuli in increasing the state of relaxation, attention, and engagement in activities (Kanehira et al, 2018;Leslie et al, 2019;Ramdinwawii & Mittal, 2017;Stappen et al, 2019;Rushambwa & Mythili, 2017;Plut & Pasquier, 2019;Soysal et al, 2020;Rushambwa & Mythili, 2017).…”

Section: Physiological and Behavioral Responses Induced By Acoustic Signalsmentioning

confidence: 99%

Affective computing in the context of music therapy: a systematic review

Santana

Lima

Torcate

et al. 2021

RSD

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Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy.

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