EEG-Based Analysis of the Emotional Effect of Music Therapy on Palliative Care Cancer Patients

Ramírez, Rafael; Planas, Josep V.; Escude, Nuria; Mercadé, Jordi J.; Farriols, Cristina

doi:10.3389/fpsyg.2018.00254

Cited by 71 publications

(92 citation statements)

References 39 publications

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“…Another study, using the same device, observed a correlation of EEG with classical music rhythm [38], [39]. Music therapy is one of the applications emanating from music-elicited emotion [40]. Moreover, in the study of auditory-related emotion, one research group reported the development of envisioned speech recognition using EEG [41].…”

Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 95%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets have been generated with the use of diverse emotion-eliciting stimuli and the resulting brainwave responses conventionally captured with high-end EEG devices. However, the applicability of these devices is to some extent limited by practical constraints and may prove difficult to be deployed in highly mobile context omnipresent in everyday happenings. In this study, we evaluate the potential of OpenBCI to bridge this gap by first comparing its performance to research grade EEG system, employing the same algorithms that were applied on benchmark datasets. Moreover, for the purpose of emotion classification, we propose a novel method to facilitate the selection of audio-visual stimuli of high/low valence and arousal. Our setup entailed recruiting 200 healthy volunteers of varying years of age to identify the top 60 affective video clips from a total of 120 candidates through standardized self assessment, genre tags, and unsupervised machine learning. Additional 43 participants were enrolled to watch the pre-selected clips during which emotional EEG brainwaves and peripheral physiological signals were collected. These recordings were analyzed and extracted features fed into a classification model to predict whether the elicited signals were associated with a high or low level of valence and arousal. As it turned out, our prediction accuracies were decidedly comparable to those of previous studies that utilized more costly EEG amplifiers for data acquisition.

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Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 95%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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“…For disabled people, it is extremely difficult or, in some cases, impossible to do day to day chores without the assistance of any caregiver. A variety of equipment and devices invented in this field for rehabilitating and aiding disabled people to cope with their physical deficiencies [7]. This technology allows disabled people to command and control external devices such as -computers, wheelchairs, robots, etc., by utilizing their thoughts.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Motor Imagery Tasks from Multi-Channel EEG Data for Brain-Computer Interface Applications

Miah

Rahman

Muhammod

et al. 2020

Preprint

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The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal part of the biosignal classification in the brain-computer interface (BCI) applications. Currently, this bio-engineering based technology is being employed by researchers in various fields to develop cutting edge applications. The classification of real-time MI-EEG signal is the core computing and challenging task in these applications. It is well-known that the existing classification methods are not so accurate due to the high dimensionality and dynamic behaviors of the real-time EEG data. To improve the classification performance of real-time BCI applications, this paper presents a clustering-based ensemble technique and a developed brain game that distinguishes different human thoughts. At first, we have gathered the brain signals, extracted and selected informative features from these signals to generate training and testing sets. After that, we have constructed several classifiers using Artificial Neural Network (ANN), Support Vector Machine (SVM), naïve Bayes, Decision Tree (DT), Random Forest, Bagging, AdaBoost and compared the performance of these existing approaches with suggested clustering-based ensemble technique. On average, the proposed ensemble technique improved the classification accuracy of roughly 5 to 15% compared to the existing methods. Finally, we have developed the targeted * Corresponding author Email address: dewanfarid@cse.uiu.ac.bd (Dewan Md. Farid) brain game employing our suggested ensemble technique. In this game, realtime EEG signal classification and prediction tabulation through animated ball are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. All relevant codes are available via open repository at: https://github.com/mrzResearchArena/MI-EEG. Keywords: Brain Computer Interface (BCI); Brain Machine Interface (BMI); Brain Engineering; Motor Imagery Electroencephalogram (MI-EEG); Motor Cortex; Clustering; Ensemble Learning. 45 are not so accurate due to the high dimensionality and dynamic behaviors of the real-time EEG data [19, 20]. Sometimes, the signals are biased with artifacts and noise due to the low conductivity of the electrodes with the scalp [1]. The objective of this paper is to extend our prior works and ameliorates the classification performance by handling multiple electrodes/ neurons data at the same 50 time. The proposed clustering-based ensemble technique clustered the dataset 3 based on the position of the electrodes so that each cluster represents dissimilar information. It also selects the model dynamically based on the electrode locations to classify real-time EEG data. We have constructed several classifiers using Artificial Neural Network (ANN), Support Vector Machine (SVM), naïve 55Bayes, Decision Tree, Random Forest, Bagging, AdaBoost and compared the performance of these existing approaches with the suggested technique. We also developed a brai...

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“…It is a medicine that is person-centered, holistic, and not just in disease as it was in the case of the biomedical technique, which fragments the individual according to the pathology, aiming only for healing and not for comfort. 6,7,13 Patients in palliative care often have too much pain that does not stop well with medication, but adjunctive therapies such as music therapy can more effectively alleviate some symptoms, such as fatigue, depression, and relatively common problems in this population. to evoke memories of pleasure and joy associated with the songs that make up the therapeutic arsenal.…”

Section: 1213mentioning

confidence: 99%

“…to evoke memories of pleasure and joy associated with the songs that make up the therapeutic arsenal. 1,7,8 It is a challenge for medical staff to deal with the pain, concerns, and reactions that come with the diagnosis. Considering this context, palliative care, associated with music therapy, acts in situations where the therapeutic part for the underlying disease is exhausted, helping to reduce pain and stress, and to recover the sense of familiarity.…”

Section: 1213mentioning

confidence: 99%

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Effectiveness and Interventions of the Music Therapy in Patients in Palliative Care

Aguilera

Melo²,

Silva

et al. 2018

AIMJ

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Currently, music therapy, as a low-cost therapeutic approach, has been successfully applied in palliative care patients, aiming to improve emotional stress and mood. Besides these advantages, the music therapy can influence the patient to have a better social interaction. In cancer patients, especially, music therapy associated with massage therapy is very costeffective, pointing to the need for the formulation of other nonpharmacological methods. Music is intrinsically related to the reduction of pain, anxiety and stress, as well as a direct relationship with spiritual improvement and quality of life. Music therapy reinforces multidisciplinary care for the patient in palliative care, and may even reduce pain in those affected.

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EEG-Based Analysis of the Emotional Effect of Music Therapy on Palliative Care Cancer Patients

Cited by 71 publications

References 39 publications

Consumer Grade Brain Sensing for Emotion Recognition

Consumer Grade Brain Sensing for Emotion Recognition

Prediction of Motor Imagery Tasks from Multi-Channel EEG Data for Brain-Computer Interface Applications

Effectiveness and Interventions of the Music Therapy in Patients in Palliative Care

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