EEG Power Spectrum Changes due to Listening to Pleasant Musics and Their Relation to Relaxation Effects.

Kabuto, Michinori; Kageyama, Takayuki; Nitta, Hiroshi

doi:10.1265/jjh.48.807

Cited by 34 publications

(28 citation statements)

References 13 publications

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“…This corresponds well to previous reports in the literature that delta, theta, alpha, and beta bands are involved in different emotional responses to music (Daly, Malik, Hwang, Roesch, Weaver, Kirke, Williams, Miranda and Nasuto 2014, Kabuto et al 1993, Lin et al 2010, Sammler et al 2007. Additionally, there is a slight right hemisphere bias to the selected features, which further supports the general understanding that the right hemisphere is more involved in the processing of emotional stimuli (Silberman and Weingartner 1986).…”

Section: Discussionsupporting

confidence: 91%

Affective brain–computer music interfacing

et al. 2016

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Abstract. ObjectiveWe aim to develop and evaluate an affective Brain-computer music interface (aBCMI) modulating the affective states of its users. ApproachAn aBCMI is constructed to detect a user's current affective state and attempt to modulate it in order to achieve specific objectives (for example, making the user calmer or happier) by playing music which is generated according to a specific affective target by an algorithm music composition system and a case-based reasoning system. The system is trained and tested in a longitudinal study on a population of 8 healthy participants, with each participant returning for multiple sessions. Main ResultsThe final online aBCMI is able to detect its users current affective states with classification accuracies of up to 65% (3 class, p< 0.01) and modulate its user's affective states significantly above chance level (p< 0.05). SignificanceOur system represents one of the first demonstrations of an online affective braincomputer music interface that is able to accurately detect and respond to user's affective states. Possible applications include use in music therapy and entertainment.

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Section: Discussionsupporting

confidence: 91%

Affective brain–computer music interfacing

et al. 2016

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“…Other researchers also observed a higher theta ERS for emotional than for neutral stimuli noting that the event-related theta band synchronization responds specifically to prolonged visual emotional stimulation (Krause et al, 2000). EEG changes in the theta range of frequencies were also observed during presentation of subjects with famous classical and commercially available ''alpha music'' (Kabuto et al, 1993) and ecologically valid environmental sounds (Shimai, 1992).…”

Section: Human Emotional Reactions and Theta Oscillationsmentioning

confidence: 94%

Motivation, emotion, and their inhibitory control mirrored in brain oscillations

Knyazev

2007

Neuroscience & Biobehavioral Reviews

750

594

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“…In 1977, researchers analyzed the power spectrum of EEG signals and found that it can reflect fluctuations in the level of alertness [6]. Scholars have also used EEG signals to evaluate mental exhaustion [7,8], identify feelings after listening to music [9] or observing various sports [10], assess emotions [11], control the movement of small mechanized vehicles, and alter the height of hospital beds.…”

Section: Related Researchmentioning

confidence: 99%

Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors

Liu

Chiang

Chu

2013

Sensors

309

170

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During the learning process, whether students remain attentive throughout instruction generally influences their learning efficacy. If teachers can instantly identify whether students are attentive they can be suitably reminded to remain focused, thereby improving their learning effects. Traditional teaching methods generally require that teachers observe students' expressions to determine whether they are attentively learning. However, this method is often inaccurate and increases the burden on teachers. With the development of electroencephalography (EEG) detection tools, mobile brainwave sensors have become mature and affordable equipment. Therefore, in this study, whether students are attentive or inattentive during instruction is determined by observing their EEG signals. Because distinguishing between attentiveness and inattentiveness is challenging, two scenarios were developed for this study to measure the subjects' EEG signals when attentive and inattentive. After collecting EEG data using mobile sensors, various common features were extracted from the raw data. A support vector machine (SVM) classifier was used to calculate and analyze these features to identify the combination of features that best indicates whether students are attentive. Based on the experiment results, the method proposed in this study provides a classification accuracy of up to 76.82%. The study results can be used as a reference for learning system designs in the future.

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EEG Power Spectrum Changes due to Listening to Pleasant Musics and Their Relation to Relaxation Effects.

Cited by 34 publications

References 13 publications

Affective brain–computer music interfacing

Affective brain–computer music interfacing

Motivation, emotion, and their inhibitory control mirrored in brain oscillations

Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors

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