Reinforcement Learning using EEG signals for Therapeutic Use of Music in Emotion Management

Dutta, Esha; Bothra, Ananya; Chaspari, Theodora; Ioerger, Thomas R.; Mortazavi, Bobak J.

doi:10.1109/embc44109.2020.9175586

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…Other studies reported that participants tend to prefer the GEW over alternative measures (Desmet et al, 2000) and to judge the GEW as clear to understand and useful in differentiating between emotions (Caicedo and Van Beuzekom, 2006). The GEW has also been used specifically to assess emotional responses to music in neuropsychological experiments (Dutta et al, 2020).…”

Section: Gewmentioning

confidence: 99%

The Role of the Mirror System in Influencing Musicians’ Evaluation of Musical Creativity: A tDCS Study

et al. 2021

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Evidence reported in the literature suggests that the mirror system not only plays a role in recognizing motor action but also fosters a better understanding of other people because it helps an individual assume another’s perspective. This led to the idea, supported by research findings, that people with higher empathy scores should show higher activation of the mirror system. Recently, it has been hypothesized that a purely auditory mirror system exists. In this study, we aimed to explore the possibility that this system might play a particular role for musicians. Specifically, this system would impact their response to a new piece of music by using non-invasive brain stimulation to modulate the activation of the mirror system. A sample of 40 young musicians was involved in this study. Half of the participants were randomly assigned to a cathodal stimulation condition, while the other half was used as a control. After listening to a new piece of music, participants were asked to rate the creativity of the piece (by focusing on how interesting, innovative, and exciting the piece was) as well as their general emotional response to it. Their empathy levels were also assessed using the Interpersonal Reactivity Index (IRI). Results showed that the cathodal stimulation of the mirror system negatively affected both the perception of creativity (level of innovation) and the emotional response to the music. There was no significant difference in the ratings of how interesting the piece was perceived. The effect was mediated by the individuals’ level of empathy. Specifically, empathic concern and fantasy dimensions increased the evaluation of creativity. Results also showed that participants reported less emotion with a negative valence in the cathodal stimulation condition.

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Section: Gewmentioning

confidence: 99%

The Role of the Mirror System in Influencing Musicians’ Evaluation of Musical Creativity: A tDCS Study

et al. 2021

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“…The returned works also presented a great diversity in the investigation of the effects of sound signals in the human organism. 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).…”

Section: Physiological and Behavioral Responses Induced By Acoustic Signalsmentioning

confidence: 99%

“…Regarding the use of Artificial Intelligence (AI) tools, only nine works related to this topic made use of these tools (Yamada & Ono, 2019;Huang & Benjamin Knapp, 2017;Wang et al, 2016;Liu et al, 2020;Dutta et al, 2020;Bhargava et al, 2020;Q. Li et al, 2020;Ibrahim et al, 2019;Liu et al, 2020).…”

Section: Physiological and Behavioral Responses Induced By Acoustic Signalsmentioning

confidence: 99%

“…Considering the types of approach for the recognition of emotions, we observed that a significant number of studies uses the analysis of physiological signals to achieve this objective. Thus, studies using biosignals such as EEG (Bankar et al, 2018;Shen et al, 2020;Dutta et al, 2020;Marimpis et al, 2020;Bo et al, 2017;Rahman et al, 2020), ECG (Hsu et al, 2020) and skin's electrodermal activity (Rahman et al, 2019) were returned. Other studies combined more than one parameter, in addition to biosignals, to assess emotion.…”

Section: Emotion Recognition Associated With Acoustic Stimulimentioning

confidence: 99%

“…Malheiro et al (2018), in turn, involved both types of problem. Another aspect observed was that the SVM was the algorithm that was most present in the returned studies (Wardana et al, 2018;Hsu et al, 2020;Lv et al, 2018;Dutta et al, 2020;Mo & Niu, 2019;Bakhtiyari et al, 2019;Lopes et al, 2019;Bo et al, 2017;Nemati & Naghsh-Nilchi, 2017;Rahman et al, 2020;Nawa et al, 2018;Dantcheva et al, 2017), followed by Artificial Neural Networks (Zhang et al, 2019;Lv et al, 2018;Dutta et al, 2020;Goyal et al, 2016;Marimpis et al, 2020;Bakhtiyari et al, 2019;Rahman et al, 2019;Rahman et al, 2020;Chapaneri & Jayaswal, 2018).…”

Section: Emotion Recognition Associated With Acoustic Stimulimentioning

confidence: 99%

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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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A Music Therapy Model Based on Consortium Blockchain Platform with Evidence and Tracking Efficacy of Hypertension Treatment

Chen,

Hsu,

2023

Lecture Notes on Data Engineering and Communications Technologies

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Reinforcement Learning using EEG signals for Therapeutic Use of Music in Emotion Management

Cited by 8 publications

References 15 publications

The Role of the Mirror System in Influencing Musicians’ Evaluation of Musical Creativity: A tDCS Study

The Role of the Mirror System in Influencing Musicians’ Evaluation of Musical Creativity: A tDCS Study

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

A Music Therapy Model Based on Consortium Blockchain Platform with Evidence and Tracking Efficacy of Hypertension Treatment

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