Impact assessment of mental subliminal activities on the human brain through neuro feedback analysis

Rushambwa, Munyaradzi Charles; Mythili, A.

doi:10.1109/icbsii.2017.8082299

Cited by 3 publications

(3 citation statements)

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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%

“…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%

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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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Section: Physiological and Behavioral Responses Induced By Acoustic Signalsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…They also configured the HC05 Bluetooth module using Cool term software through AT commands to receive the data wirelessly from the brainwave sensor and passed it via UART to the controller. Finally, they implemented Hardware circuitry in Proteus software (Rushambwa and Mythili 2017). Mehmood, Du, and Lee (2017) recognized the emotion using maximum characteristics of features and methods which extracted from deep learning research domains.…”

Section: Research Backgroundmentioning

confidence: 99%

Efficient Method to Improve Human Brain Sensor Activities Using Proposed Neuroheadset Device Embedded With Sensors: A Comprehensive Study

Chowdhury

Saif

2019

IJSECS

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The main purpose of this research is to investigate the human brain sensor activities related prior researches towards the needs of an efficient method to improve the human brain sensor activities. Human brain activities mainly measured by brain signal acquired from the brain sensor electrodes positioned on several parts of the brain cortex. Although previous researches investigated human brain activities in various aspects, the improvement of the human brain sensor activities is still unsolved. In today's world, it is very crucial need for improving the sensor activities of the human brain using that human brain improved signal externally. This research demonstrated a comprehensive critical analysis of human brain activities related prior researches to claim for an efficient method integrated with proposed neuroheadset device. This research presented a comprehensive review in various aspects like previous methods, existing frameworks analysis and existing results analysis with the discussion to establish an efficient method for acquiring human brain signal, improving the acquired signal and developing the sensor activities of the human brain using that human brain improved signal. Demonstrated critical review has expected for constituting an efficient method to improve the performance of maneuverability, visualization, subliminal activities and so forth on human brain activities.

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